{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "# Postprocessing\n",
    "This notebook summarizes performance metrics of a trained GCN model. It especially plots:\n",
    "* ROC/PR curves\n",
    "* The relationship of predictions and node degree\n",
    "* The overlap of predictions and database cancer genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import sys, os\n",
    "import operator\n",
    "\n",
    "sys.path.append(os.path.abspath('../EMOGI'))\n",
    "import gcnIO, postprocessing\n",
    "\n",
    "sys.path.append(os.path.abspath('../pancancer/preprocessing'))\n",
    "import preprocessing_utils as pre_utils\n",
    "\n",
    "# set options\n",
    "np.set_printoptions(suppress=True)\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)\n",
    "\n",
    "import matplotlib.pyplot as plt, seaborn as sns\n",
    "plt.rc('font', family='Helvetica')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Relevant Paths\n",
    "\n",
    "* `model_dir`: The directory were a trained EMOGI model is located in. Can be set with the training script.\n",
    "* `network_name`: The name of the PPI network used. This can be any of: CPDB, Multinet, PCNet, IREF (for IRefIndex from 2015), IREF_new (for a current version of IRefIndex) and STRING (for STRING-db) and should fit with the network used for training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_dir = '../data/GCN/training/Rev1_CNA_separated_all_networks/CPDB/'\n",
    "#model_dir = '../data/GCN/training/final_TCGA_all_networks/CPDB/multiomics/'\n",
    "network_name = 'CPDB'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data comes from ../../../../pancancer/rev1_container_all_networks_CNA_separated/CPDB_multiomics_cnaseparate_samesplit.h5\n",
      "{'epochs': 5000, 'lr': 0.001, 'support': 1, 'hidden_dims': [300, 100], 'loss_mul': 45.0, 'decay': 0.005, 'dropout': 0.5, 'data': '../../../../pancancer/rev1_container_all_networks_CNA_separated/CPDB_multiomics_cnaseparate_samesplit.h5', 'cv_runs': 10}\n"
     ]
    }
   ],
   "source": [
    "args, data_file = gcnIO.load_hyper_params(model_dir)\n",
    "data = gcnIO.load_hdf_data(os.path.join(model_dir, data_file))\n",
    "adj, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "print (\"Data comes from {}\".format(data_file))\n",
    "print (args)\n",
    "CLASSIFICATION_THRESHOLD = 0.7337269395589828\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Predictions\n",
    "I want to see what the GCN predicts and if those predictions make any sense."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Read predictions from 10 CV runs\n"
     ]
    }
   ],
   "source": [
    "all_preds, all_sets = postprocessing.compute_ensemble_predictions(model_dir)\n",
    "#postprocessing.compute_node_degree_relation(model_dir)\n",
    "postprocessing.compute_average_ROC_curve(model_dir, all_preds, all_sets)\n",
    "postprocessing.compute_average_PR_curve(model_dir, all_preds, all_sets)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pd.read_csv(os.path.join(model_dir, 'ensemble_predictions.tsv'),\n",
    "                                    sep='\\t', header=0)\n",
    "nodes = pd.DataFrame(node_names, columns=['ID', 'Name']).set_index('ID')\n",
    "nodes = nodes[~nodes.index.duplicated()]\n",
    "pred_ordered = predictions[~predictions.index.duplicated()]\n",
    "pred_ordered.reindex(index=nodes.index)\n",
    "predictions.set_index('ID', inplace=True)\n",
    "predictions.drop([c for c in predictions.columns if c.startswith('Prob_pos')], axis=1, inplace=True)\n",
    "predictions.columns = ['Name', 'label', 'Num_Pos', 'Prob_pos', 'Std_Pred']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Enrich predictions and plot relationship to node degree"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted 4239 genes of 13627 total to be involved with cancer\n"
     ]
    }
   ],
   "source": [
    "features_df = pd.DataFrame(features, index=node_names[:, 0])\n",
    "features_df['Name'] = node_names[:, 1]\n",
    "features_df['neighbors'] = adj.sum(axis=0)\n",
    "pred_with_feat = features_df.join(predictions, how='inner', lsuffix='_')\n",
    "pos_predicted = pred_with_feat[pred_with_feat.Prob_pos > CLASSIFICATION_THRESHOLD]\n",
    "\n",
    "# add columns to distinguish between training and test set\n",
    "labels_df = pd.DataFrame(node_names, index=node_names[:, 0], columns=['ID', 'Name']).drop(['ID'], axis=1)\n",
    "labels_df['positive'] = (y_train[:,0] | y_test[:,0] | y_val[:, 0])\n",
    "labels_df['training_set'] = train_mask\n",
    "labels_df['testing_set'] = test_mask\n",
    "labels_df['validation_set'] = val_mask\n",
    "labels_df['label'] = np.logical_or(train_mask, test_mask, val_mask).astype(int)\n",
    "labels_df['negative'] = labels_df.label & ~labels_df.positive\n",
    "\n",
    "# show the ones that are most confidently predicted\n",
    "print (\"Predicted {} genes of {} total to be involved with cancer\".format(pos_predicted.shape[0], predictions.shape[0]))\n",
    "pos_nofeat = predictions[predictions.Prob_pos >= CLASSIFICATION_THRESHOLD]\n",
    "pos_nofeat.sort_values(by='Prob_pos', ascending=False).to_csv(os.path.join(model_dir, 'positive_prediction.txt'),\n",
    "                                                                 sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicted 714 out of 787 known infection genes\n",
      "Out of these 787, 199 were not shown during training.\n",
      "Predicted 144 out of 199 test genes (72.36180904522614%)\n",
      "Predicted 515 out of 537 train genes (95.90316573556798%)\n",
      "Correctly rejected 1685 out of 1991 genes (True Negatives) (84.63083877448518%)\n",
      "Falsely predicted 306 negatives (FN)\n"
     ]
    },
    {
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       "      <td>False</td>\n",
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       "      <td>False</td>\n",
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       "      <td>0.998</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
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       "      <th>ENSG00000136485</th>\n",
       "      <td>DCAF7</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>DCAF7</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000135913</th>\n",
       "      <td>USP37</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>USP37</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000141376</th>\n",
       "      <td>BCAS3</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>BCAS3</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ENSG00000163517</th>\n",
       "      <td>HDAC11</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>HDAC11</td>\n",
       "      <td>False</td>\n",
       "      <td>10</td>\n",
       "      <td>0.998</td>\n",
       "      <td>0.002</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "                  Name_  positive  training_set  testing_set  validation_set  \\\n",
       "ENSG00000100603    SNW1     False          True        False           False   \n",
       "ENSG00000134308   YWHAQ     False         False         True           False   \n",
       "ENSG00000105325    FZR1     False          True        False           False   \n",
       "ENSG00000158092    NCK1     False          True        False           False   \n",
       "ENSG00000114166   KAT2B     False          True        False           False   \n",
       "ENSG00000196363    WDR5     False          True        False           False   \n",
       "ENSG00000100629  CEP128     False         False         True           False   \n",
       "ENSG00000135250   SRPK2     False         False         True           False   \n",
       "ENSG00000188612   SUMO2     False         False         True           False   \n",
       "ENSG00000107854   TNKS2     False          True        False           False   \n",
       "ENSG00000101004    NINL     False          True        False           False   \n",
       "ENSG00000145386   CCNA2     False          True        False           False   \n",
       "ENSG00000141068    KSR1     False          True        False           False   \n",
       "ENSG00000120693   SMAD9     False          True        False           False   \n",
       "ENSG00000075218   GTSE1     False          True        False           False   \n",
       "ENSG00000135945    REV1     False          True        False           False   \n",
       "ENSG00000136485   DCAF7     False          True        False           False   \n",
       "ENSG00000135913   USP37     False          True        False           False   \n",
       "ENSG00000141376   BCAS3     False          True        False           False   \n",
       "ENSG00000163517  HDAC11     False         False         True           False   \n",
       "\n",
       "                 label_  negative    Name  label  Num_Pos  Prob_pos  Std_Pred  \n",
       "ENSG00000100603       1      True    SNW1  False       10     1.000     0.000  \n",
       "ENSG00000134308       1      True   YWHAQ  False       10     1.000     0.000  \n",
       "ENSG00000105325       1      True    FZR1  False       10     1.000     0.000  \n",
       "ENSG00000158092       1      True    NCK1  False       10     1.000     0.000  \n",
       "ENSG00000114166       1      True   KAT2B  False       10     1.000     0.000  \n",
       "ENSG00000196363       1      True    WDR5  False       10     1.000     0.000  \n",
       "ENSG00000100629       1      True  CEP128  False       10     1.000     0.000  \n",
       "ENSG00000135250       1      True   SRPK2  False       10     1.000     0.000  \n",
       "ENSG00000188612       1      True   SUMO2  False       10     1.000     0.000  \n",
       "ENSG00000107854       1      True   TNKS2  False       10     1.000     0.000  \n",
       "ENSG00000101004       1      True    NINL  False       10     1.000     0.000  \n",
       "ENSG00000145386       1      True   CCNA2  False       10     1.000     0.000  \n",
       "ENSG00000141068       1      True    KSR1  False       10     0.999     0.001  \n",
       "ENSG00000120693       1      True   SMAD9  False       10     0.998     0.001  \n",
       "ENSG00000075218       1      True   GTSE1  False       10     0.998     0.002  \n",
       "ENSG00000135945       1      True    REV1  False       10     0.998     0.001  \n",
       "ENSG00000136485       1      True   DCAF7  False       10     0.998     0.002  \n",
       "ENSG00000135913       1      True   USP37  False       10     0.998     0.002  \n",
       "ENSG00000141376       1      True   BCAS3  False       10     0.998     0.002  \n",
       "ENSG00000163517       1      True  HDAC11  False       10     0.998     0.002  "
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels_with_pred = labels_df.join(predictions, lsuffix='_', how='inner')\n",
    "predictions_for_pos = labels_with_pred[labels_with_pred.positive == 1]\n",
    "true_positives = predictions_for_pos[predictions_for_pos.Prob_pos > CLASSIFICATION_THRESHOLD]\n",
    "no_tp = true_positives.shape[0]\n",
    "no_positives = predictions_for_pos.shape[0]\n",
    "pred_pos_test = true_positives[true_positives.testing_set == 1]\n",
    "pred_pos_train = true_positives[true_positives.training_set == 1]\n",
    "predictions_for_neg = labels_with_pred[labels_with_pred.negative == 1]\n",
    "true_negatives = predictions_for_neg[predictions_for_neg.Prob_pos <= CLASSIFICATION_THRESHOLD]\n",
    "no_tn = true_negatives.shape[0]\n",
    "no_fn = predictions_for_neg[predictions_for_neg.Prob_pos > CLASSIFICATION_THRESHOLD].shape[0]\n",
    "\n",
    "print (\"Predicted {} out of {} known infection genes\".format(no_tp, no_positives)\n",
    "      )\n",
    "print (\"Out of these {}, {} were not shown during training.\".format(no_positives,\n",
    "                                                                    y_test[:, 0].sum())\n",
    "      )\n",
    "print (\"Predicted {} out of {} test genes ({}%)\".format(pred_pos_test.shape[0],\n",
    "                                                        y_test[:,0].sum(),\n",
    "                                                        pred_pos_test.shape[0]/y_test[:,0].sum()*100.)\n",
    "      )\n",
    "print (\"Predicted {} out of {} train genes ({}%)\".format(pred_pos_train.shape[0],\n",
    "                                                         y_train[:,0].sum(),\n",
    "                                                         pred_pos_train.shape[0]/y_train[:,0].sum()*100.)\n",
    "      )\n",
    "print (\"Correctly rejected {} out of {} genes (True Negatives) ({}%)\".format(no_tn,\n",
    "                                                                     predictions_for_neg.shape[0],\n",
    "                                                                     no_tn/predictions_for_neg.shape[0]*100.))\n",
    "print (\"Falsely predicted {} negatives (FN)\".format(no_fn))\n",
    "predictions_for_neg.sort_values(by='Prob_pos', ascending=False).head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Prob_pos'>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HzKEei/PQ/HRMTJ5ovhiaDsO2Vzx2bxERERFP6s6IzHPAZac7wTAMP+D3wIcuyCTiO45OKxt31QkPL80pY1tRDV+/YBQBfp4b+EyJGsD954/kr/viqI9Mg7X/0FLMIiIi0ied8R2WaZrLgcNnOO3rwGtAmStCifiMHW9AynSISD72kGma/HnJHoYMHsA1mUkej/SlucMZFh3K35sugtLtkLfS4xlERERE3K3XHxUbhpEEXA1ovVfpX8r3WIXCuBNXK/tgRyk7D9XywIWj8PfgaMxRQf5+/GLhOJ6uyaLJPwLWPenxDCIiIiLu5op3WX8Bvm+apvNMJxqGca9hGBsMw9hQXl7ugluL2GjHG4AB6Vcee8jpNPnL0j0MjxnIwkmJtkWbOzqGCyek8lLrTMzd70PjmQZVRURERHyLKwqZLOBFwzDygOuAxw3DuKqzE03TfNI0zSzTNLNiYmJccGsRG+14HYbMhPCEYw+9v72EXSV1to3GHO8n89JZZJ6L4WyzsoqIiIj0Ib1+p2Wa5jDTNIeapjkUeBX4qmmab/b2uiJerSwHynedsFpZx5HRmFGxocyfaN9ozFGJkSFcfMHF7HKmULPmP3bHEREREXGp7iy//D9gNTDGMIxCwzDuNgzjPsMw7nN/PBEvtf11MBwwduGxh97ZWkxuWT3fvGg0fg7DxnCfu3vOcD4NuYCIymxay3LtjiMiIiLiMv5nOsE0zZu7ezHTNO/sVRoRX2CaVn/MkFkQFgdYozF//SiXtPgwLh8fb3PAzwX6Oxh/2ZfoePPf5H74FONu+4PdkURERERcwt5J/CK+qGIPVOaesHfMoi1F7C9v4JsXjcLhJaMxR83MGM+WwEyi9r1Be3u73XFEREREXEKFjEhP7VtmHUdefOyhZ1YeYExcGJeke89ozFGGYeCfeTMJZhmfLXvH7jgiIiIiLqFCRqSn9i+DQcNg0BAAdhTXsL2ollump3rdaMxR4y+4hUZCqF/3Hzqcpt1xRERERHpNhYxIT3S0Qd5KGHH+sYde2VBIoL+DKzPsX6msK47gUCqGXM7c1s9YnH3A7jgiIiIivaZCRqQnCjdAaz0MtwqZlvYO3swu4tJx8UQOCLQ53OklnftFwowmtiz5L06NyoiIiIiPUyEj0hP7l1nLLg+bA8CSnaVUN7ZxQ1ayzcHOzG/obBpDEphR/yEf7iy1O46IiIhIr6iQEemJfcsgMRNCBgHw8oZCkiJDmDki2uZg3eBwEDzlFub6beP5peswTY3KiIiIiO9SISPSXc01ULTx2LSy4uomVuSWc+2UZK/ZAPNMHBk344eTMeWLWba7zO44IiIiImdNhYxId+WtBLPjWKP/qxsLMU24for3Tys7JnoUzqQsbgr8jEc+2qtRGREREfFZKmREumvfMggYAMnTcDpNXtlYwKyRg0mJGmB3sh5xTLyRUWYedYU7WLm3wu44IiIiImdFhYxId+1fBkNmgX8gaw5UUnC4iRuyUuxO1XPpCzExuHHABv7x6X6704iIiIicFRUyIt1RXQCVe49NK3t5fQFhwf5cOi7e5mBnISweI3UG1wZtYOXeCvaW1dmdSERERKTHVMiIdMf+T6zj8POpaWrj/e0lXJmRSHCAn62xztq4qxjcuI80v0P8a9VBu9OIiIiI9JgKGZHu2L8MQuMgdixvbymmpd3JjVmpdqc6e2MXAgYPJO7gtU2F1Da32Z1IREREpEdUyIicidNpjcgMPw8Mg1c2FJAWH8b4pHC7k5298ARIPYfz2lfR2NrBqxsK7U4kIiIi0iMqZETOpHQbNFbC8PPZU1rHlsIabshKwTB8Y++YLqVfRUjVLuYn1vPv1Xk4nVqKWURERHyHChmRMznWH3Me7207hGHAgkmJtkZyifSFAHwtbjt5lY18mltucyARERGR7lMhI3Im+5ZBTBqEJ/DhjlKmpA4iJizI7lS9F54IKecwpvIjYsKC+NeqPLsTiYiIiHSbChmR02lrhvzVMPx8Cg43svNQLZeMi7M7leuMuwpH2Q6+NtHkk93lHKhosDuRiIg018Du92Hxj+CJOfDsZZC/xu5UIl5HhYzI6eSvhvZmGHE+H+4sBeCSdB/cO6YrY63pZdcFbyDAz9CojIiIXUwTVv4F/nEu/H4o/O8mWP80BEdAdT48eym8ejfUFNmdVMRrqJAROZ2Dq8BwwJCZfLCjhDFxYQyNHmh3KteJSIKU6YTufZd5ExJ4dWMh9S3tdqcSEelfTBMW/wCW/gz8g2Hu9+DOd+EH+XDnO3D/epj7Xch5Gx7NguUPWzMGRPo5FTIip1OwBuLGU9kWyIa8w1zal6aVHZV+FZRu495xTupb2nl9k5ZiFhHxGNOEj34Ba5+AGffDXYvh/B/C0NkQEGydEzgQLvgJ3L8ORl4IH/8aHj8H6krszS5iMxUyIl3paIfCjZAynY9yynCacMm4PjSt7Kj0K61D1cdMSo7gX6vyME0txSwi4hHL/w9W/hmy7oJLfg2nW9p/0FC48Xm4/U2riHnrfqsQEumnVMiIdKVsB7Q1QOo5fLizhKTIEMYl+vAmmF05Mr2MHW9xx8yh7CtvYPW+SrtTiYj0fasehWW/hkm3wBV/PH0Rc7wR58Mlv4K9S6w+GpF+SoWMSFfy1wLQGDeF5bkVXJwe5/ubYHblyPSyeUmNRIQE8OL6ArsTiYj0beufhg9/DOOuhisfBUcP35JNvQdGXgQf/gTK97gno4iXUyEj0pWCtRCWyKclQbS2O7m0L04rO+rI5phBue9xdWYSi7eXUNXQanMoEZE+6sAKePc7MOYKuOYpcPj1/BqGAVc+BgED4PV7oF0/s6X/USEj0pWCtZAyjQ9zyhg0IICpQwfZnch9IpIhIQN2vcuNU1No7XDyxmYt8Ski4nLODlj8Q4hIheueBb+As79WWDwsfAQObYFPf+e6jCI+QoWMSGdqiqCmgI7k6XyUU8qFY+Pw9+vj/7mkzYfCdYwNbWRSSiQvrS9Q07+IiKttfh5Kt8Elv4SAkN5fb+wCyLzNWjBAm2ZKP9PH35mJnKXCdQBsc6RR29zOJel9cNnlk42dbx13v8dNU1PYXVpHdkG1rZFERPqU5lr4+FeQOsPqTXSVy34Hkanw+r3WPUT6CRUyIp3JXwv+IbxxKIqQAD/mjo6xO5H7xaRB1HDIeYcFkxIZEOjHi+vU9C8i4jIr/wQN5XDpb7u/Qll3BIXB1U9CdT6sesR11xXxcipkRDpTsBYzaTIf5FQyd3Q0wQFn0YjpawwD0ubBgeWEmg3Mn5jA21uLqW9ptzuZiIjvq8qD1Y9ZSy0nTXb99VOnW4sHrH8GWhtdf30RL6RCRuRkrY1QspWyyAxKapv79mplJ0tbAM42yF3CTdNSaWzt4J0txXanEhHxfUseAoc/XPiQ++4x835oOgxbX3TfPUS8iAoZkZMVbwJnOyuah+PnMLggLdbuRJ6TnAUDY2HXO2SmRDI6LpT/aU8ZEZHeyfsMdr4Fs78F4Qnuu0/qDEjMhNWPg9PpvvuIeAkVMiInK7A2wvxvcTzTh0UROSDQ5kAe5PCDMZdD7hKMjlZunJrKloJqcg6peVRE5Kw4nfDBDyE8GWbc7957GYZ1j8pc2LvEvfcS8QIqZEROlr+WtqhRbCo3OH9MPxqNOWrsAmith/2fcnVmEoF+Dl7SqIyIyNnZ9rK1z8tFP4fAAe6/X/qVEJ4Eqx91/71EbKZCRuR4TicUrqNg4AQAZo+KtjmQDYbNhcBQ2PUOUQMDuXR8PG9sLqK5rcPuZCIivsU0YdWjEDceJlznmXv6BcD0L8OB5XBoq2fuKWITFTIix6vMhaYqVreNIjo0iLT4MLsTeZ5/EIy6GHa/B84ObpqaQk1TGx/sKLE7mYiIb8lfY21+Oe1e1y63fCaT74CAgbDmcc/dU8QGKmREjnekP+bVskTmjIrG8OQvHm+SNt/a66BwPTOGDyYlKkR7yoiI9NS6JyE4AiZc79n7hkTC5Nth26tQe8iz9xbxIBUyIscrWEt70CA2N0Yzpz9OKztq1MXgCIBd7+BwGFw3OYXV+ysprNLeBCIi3VJ7CHIWQebtnumNOdn0+8DZDuuf8vy9RTxEhYzI8fLXUhg6ATCYPbIfFzLBETD8XMh5B0yTayYnAfDGpiKbg4mI+IiNz4GzA6bebc/9o4bB2Pmw4VlobbAng4ibqZAROaqhEipzWdM2krT4MGLDg+1OZK+0eVB1AMpySIkawPRhUby+uQjTNO1OJiLi3dpbYeM/YdQlEDXcvhwzvg5NVbDlf/ZlEHEjFTIiRxWuA2DR4ZT+PRpz1JgrAAN2vQPAtVOSOVDRwKb8KntziYh4u5xFUF9qNfnbKWWatUHmxufszSHiJipkRI4qWIvTEcDG9mHMGR1jdxr7hcVDchbseheAKyYkEBLgx6sbNb1MROS01j1ljcSMuMDeHIZhLTRQsg0q99mbRcQNVMiIHFW4gUPBIzH9gpk2NMruNN5hzBVwKBtqiggN8uey8fG8s7VYe8qIiHTl0BYoWANTvwQOL3iblX6lddzxhr05RNzAC/4LE/ECTicUZ7OpfRhThw0iJNDP7kTeIW2eddz9HgDXTk6mrrmdJTtLbQwlIuLF1j0FAQMg4xa7k1gikiF5Gux80+4kIi6nQkYE4PA+aK3j04YUZo/UtLJjokdD1IhjhcyMEYNJiAjmtU2FNgcTEfFCjYdh2ysw8UZrLxdvMe4qTS+TPumMhYxhGM8ahlFmGMb2Lp6/1TCMrYZhbDMMY5VhGJNcH1PEzYo3A7DVObx/7x9zMsOAtCvgwAporsHPYXB1ZhLL95RTVttsdzoREe+y+Xlob4ZpX7I7yYk0vUz6qO6MyDwHXHaa5w8A55qmOQH4FfCkC3KJeFbRJlqMYKpDhpKeEG53Gu8yZh4422DvUsBavcxpwpvZavoXETnGNK3VwVJnQtw4u9OcSNPLpI86YyFjmuZy4PBpnl9lmubR9VjXAMkuyibiMWbxZnLMoZwzKg6Hw7A7jndJmQYDomGXNb1sREwoGSmRvLZRe8qIiBxTvNmappxxs91JOqfpZdIHubpH5m7gfRdfU8S9OtoxD21hU/tQZmta2akcfjD6MshdAh1tgDUqs7u0jh3FtTaHExHxEttfA0cAjF1gd5LOaXqZ9EEuK2QMwzgfq5D5/mnOudcwjA2GYWwoLy931a1FeqdiD472JvXHnE7aFdBSA3krAVgwMYFAPwevblTTv4gIzg6rkBl1MYQMsjtN5zS9TPoglxQyhmFMBJ4GrjRNs7Kr80zTfNI0zSzTNLNiYrQylHiJI43+NYPGkxARYnMYLzX8fPAPObZ6WeSAQC5Kj2XRlmJa2502hxMRsdnBVVB3CCZcZ3eS09P0Muljel3IGIaRCrwO3G6a5p7eRxLxrPbCTdSbIQwdM9HuKN4rcACMON/qkznSF3Pt5GQON7Tyye4ym8OJiNhs+6sQMBBGX253ktPT9DLpY7qz/PL/gNXAGMMwCg3DuNswjPsMw7jvyCkPAYOBxw3DyDYMY4Mb84q4XGPeBrY5hzFndKzdUbzbmCugthBKtgIwd3QM0aGB2lNGRPq39lbY+ZY1BTdwgN1pTk/Ty6SP8T/TCaZpnnb5DdM07wHucVkiEU9qb2XA4Z1s5xJuGTbY7jTebfRlgGGNyiRMIsDPwZUZSfx7dR5VDa0MGhhod0IREc/bvwyaqmC8l08rO2rcVfDBj6zpZYNH2J1GpFdcvWqZiG8pz8HfbKVm0DgGBp2xru/fQmMgZTrsfvfYQ9dOTqatw2TRlmIbg4mI2GjbqxAcCSMusDtJ92h6mfQhKmSkX2vN3wjAwGFTbU7iI9KusBpFq/MBSE8MZ2xCuKaXiUj/1NoIu961igN/HxmV1vQy6UNUyEi/djh3LTXmANLS1OjfLWPmWcfdn28Xde3kJLYW1pBbWmdTKBERm+x5H9oavH+1spOlL7Q+lKopsjuJSK+okJF+zTiUzTZzOFOGRdkdxTdEj4To0dYnkEdcmZGEn8PgVY3KiEh/s+01CEuAIbPsTtIzIy60jvuX2ZtDpJdUyEj/1dbM4Ia9HBqQRnhwgN1pfMeYK+DgZ9BUDUBMWBDnjY7hzc1FdDhNe7OJiHhKUzXsXQLjrgGHn91peiZ2LITGw76P7U4i0isqZKTfaj20DX/aIWmy3VF8S9o8cLZD7pJjD107JZnS2hZW7q2wMZiIiAflvA0drTDhWruT9JxhWIsT7FsGTm1qLL5LhYz0W8U7VwEQlzbD5iQ+JikLBsaesHrZhWNjiQgJ4LWNml4mIv3Etldg0DBI9NEPw0ZcAE2HoWSL3UlEzpoKGem3Gg+sp8IMZ8LYcXZH8S0OB4y5DHKXQnsLAEH+fiyYlMAHO0qobW6zOaCIiJvVl0PeCqvJ3zDsTnN2hp9nHTW9THyYChnpt0IPb2d/wCgGhQbZHcX3jJkHrXXWL/Ijrp2cTEu7k/e2HrIxmIiIB+x5H0wnjF1od5KzFxoD8ROt6WUiPkqFjPRLbc31JLXm0Rg9we4ovmn4uRAwAHa9d+yhjJRIhscM1J4yItL37XoPIlIh3sd/h4y4APLXQEu93UlEzooKGemXDmxbg59hMnDoNLuj+KaAEOsX4O73wbRWKjMMg2snJ7M+r4qDlQ02BxQRcZPWBmvZ4jGX++60sqNGXADONmslShEfpEJG+qXyPWsAGD5pts1JfFjaPKgrhuLNxx66ZnIShgGvb9ImayLSR+1bBu3NkHaF3Ul6L/Uc8A9Rn4z4LBUy0i8ZxZupMAYxOGGI3VF816hLwXDA7s+nlyVEhDBrRDSvbSrEqT1lRKQv2v0eBEf43iaYnfEPgqGzVciIz1IhI/1Oh9Mkvj6HstB0u6P4toGDIXXGCX0yANdOSaKwqol1eYdtCiYi4ibODtizGEZdAn59ZCPlERdAxR6oLrA7iUiPqZCRfmd3/iGGUowjKcPuKL5vzBVQtgOq8o49dOm4eAYG+mlPGRHpewrWQmOl9bOvrxhxgXXcr9XLxPeokJF+Z9/2tTgMk9jRavTvtaNzxI8blRkQ6M8VExJ4b9shGlvbbQomIuIGu94FRwCMvMjuJK4TMwbCEjW9THySChnpd+ryNgEQNSLL5iR9QNRwiBl7Qp8MwLVTkmlo7eCDHSU2BRMRcTHTtH7WDZsLweF2p3Edw4AR58P+T6ypcyI+RIWM9CtOp0lwxQ4a/MIhPMnuOH1D2hVwcBU0ft4TM21oFClRIby2UauXiUgfUbEHDu+3ll3ua0ZcAE1VcCjb7iQiPaJCRvqV3LJ6RjgPUD8o3ffX//cWY+aB2QG5Hx57yOEwuCYzmc/2VVBc3WRjOBERF9n1rnXsS/0xRw0/zzpqepn4GBUy0q+s31dKmlFASEqG3VH6jsRMCI3//Jf8EddOTsY04Y3NGpURkT5g93uQkAERfXA0f2A0JEyy9sgR8SEqZKRfyduzhSCjjbChmXZH6TscDmuqxd6PoK352MOpgwcwbWgUr20sxDS1p4yI+LC6UijcYG0E3FeNuMBala2lzu4kIt2mQkb6DdM0aS3MBsBImGRvmL4mbR60NcCB5Sc8fO2UJPZXNLC5oNqeXCIirrDnfcDsm9PKjhpxATjbIe8zu5OIdJsKGek38g83ktKyl3ZHEAweZXecvmXYXAgMhd0nTi+7YkICwQEOXtWeMiLiy3a9B5GpEDfO7iTukzzVWlo6f5XdSUS6TYWM9Bsb8qpINw7SNjgN/PztjtO3+AfByAth9/vgdB57OCw4gMvGxfPOlmKa27Ssp4j4oJZ6a2niMfP69iIxASGQNBkOrrY7iUi3qZCRfmPjwcOM8ztIsBr93WPMPKgvheJNJzx87ZRkapvbWZpTalMwEZFe2L8MOlo+3wC4L0udAcWboU2rTYpvUCEj/UZB3h4iqceIn2B3lL5p1MVg+J2yetnMEdHEhwfzmqaXiYgv2vMBBIVbb/L7uiEzwdlmLWwg4gNUyEi/UNPURnDlTusvavR3jwFR1i/B3e+d8LCfw+DqyUksz62grLa5ixeLiHgh07RWZBxxPvgF2J3G/VKmAwbka3qZ+AYVMtIvZBdUk04eJgbEptsdp+9Kmwflu6By3wkPXzs5mQ6nyZvZ2lNGRHxI6Q6oK4aRF9udxDNCIq0FDQ6q4V98gwoZ6Rc2Hqwi3XEQM2oEBIXaHafvOro06UmjMiNjQ8lMjeSVDdpTRkR8SO6H1nHkRfbm8KTUGVC4Hjra7U4ickYqZKRf2HjwMBkB+TgSJtodpW8bNATixltLlZ7khqwUcsvqydaeMiLiK/YuhfgJEJ5gdxLPGTIDWuuhZKvdSUTOSIWM9HntHU725xcS5yyzfiGJe425AgrWQEPlCQ/Pn2jtKfPyBjX9i4gPaKqG/DX9Z1rZUakzraP6ZMQHqJCRPm93aR1D2g9Yf9GIjPulXQGmE/YsPuHhsOAArpiQwNtbimlq1Z4yIuLl9n8CZgeMusTuJJ4VngCDhqpPRnyCChnp8zYerGKckWf9JV6FjNslZEB40il9MmBNL6tvaef97Yc8n0tEpCf2LoHgCGvH+/4mdaY1IqOeRvFyKmSkz9t4sIrJQYWYofEQGmt3nL7PMGDM5bDv41M2VZs+LIqhgwfw8oYCm8KJiHSDaULuUhh+Pvj5253G84bMgMZKqNhjdxKR01IhI33ehrwqMvzztRGmJ425AtoarakZxzEMg+uzUliz/zAHKxvsySYiciYl26C+pP9NKzvqaJ+MppeJl1MhI31aSU0zFdU1JLTlq9Hfk4bOsXbC3vXuKU9dMzkJhwGvqOlfRLzV3iXWsT8tu3y8wSNgYIwa/sXrqZCRPm1TfhUjjSIcZrsa/T3JP9B6A7BnMThPbOxPiAhh7ugYXt1YSIdT869FxAvlLrF6KsPi7E5iD8Ow9pM5qEJGvJsKGenTNuRVkRFw0PqLGv09K20eNJRD4YZTnrohK4WS2mZW5JbbEExE5DSaqqBgXf+dVnbUkJlQkw81Gj0X76VCRvq0jflVzA4tgcBQGDTM7jj9y8iLwBEAu94+5akLx8YyaECAppeJiPfZt+zIssv9bP+Yk6XOsI4alREvpkJG+qzmtg52FNUwwS/P2m3eoX/uHhUSCcPPhZ2LTlnCM8jfj6szk/lwZwmHG1rtySci0pm9SyE4EpKy7E5ir/gJEBgG+Wr4F++ld3bSZ20pqKbD2UF80z41+ttl7EKoPgglW0956oapybR1mLyVXWRDMBGRTjidViEz4oL+uezy8Rx+kDJNIzLi1VTISJ+1Mb+KVKMM//YGNfrbJW0eGA5rVObkp+LDmZgcwUvrCzC16ZqIeIOSrVBfqmllRw2ZCeU50HjY7iQinVIhI33WpoNVXBBRYv0lbry9YfqrgdEwZBbknFrIAFyflcKukjq2FdV4OJiISCf6+7LLJxtyZD+Z/DX25hDpggoZ6ZNM02TjwSpmhZVYIwKxY+2O1H+lX2ntDl2265SnrsxIJCTAj/+uzbchmIjISfZ+BAmTIDTW7iTeIXEy+AVqPxnxWipkpE/aX9FAVWMbYx0FMHgUBITYHan/SptvHTsZlQkPDmDBpAQWbSmmrrnNw8FERI7TXGMtu6zRmM8FBENCRqfL6It4AxUy0idtPFgFQGxjLsSNszlNPxeeACnTO+2TAbh1+hAaWzt4M7vYw8FERI5zYLm17PKIC+xO4l2Ss6B4M3TowybxPmcsZAzDeNYwjDLDMLZ38bxhGMYjhmHsNQxjq2EYk10fU6RnNh2sIjmklYC6QohXf4ztxi6E0m1weP8pT01MjmBcYjj/XZuvpn8Rsc++j609x5Kn2Z3EuyRnQXsTlO6wO4nIKbozIvMccNlpnr8cGHXk617g772PJdI7m/OruSL2yCorcVp62XZjF1jHTkZlDMPglump5ByqJbug2rO5RETA2utq70cwbC74B9qdxrskT7WOhevtzSHSiTMWMqZpLgdOt+7elcC/TcsaINIwjARXBRTpqbrmNvaU1TFj4NEVyzS1zHaDhljzrLtYvezKjCQGBqrpX0Rscni/teeVppWdKiIFQuOgaKPdSURO4YoemSSg4Li/Fx55TMQW2wprME1Ic+RDyCAIT7Q7kgCkL7R+EdYUnvJUaJA/CzOSeHtrMTVNmoctIh6272PrqELmVIYBSVkakRGv5NFmf8Mw7jUMY4NhGBvKy8s9eWvpRzYfmZ4U05Br7R9jGPYGEsvYK61jztudPn3r9FSa25y8ubnIg6FERLCmlQ0aCoNH2J3EOyVnQeVebYwpXscVhUwRkHLc35OPPHYK0zSfNE0zyzTNrJiYGBfcWuRUm/OrGRkdgn9FjjbC9CbRIyE2vcvVy8YnRTAxOUJN/yLiWe2tkLcCRlxodxLvdbRPRtPLxMu4opBZBHzhyOpl5wA1pmkecsF1RXrMNE2yC6q4MK4R2hrVH+Ntxi60NlarK+306VumpbK7tI5N+VUeDiYi/VbBWmit17Sy00nMtDaX1n4y4mW6s/zy/4DVwBjDMAoNw7jbMIz7DMO478gp7wH7gb3AU8BX3ZZW5AwKq5qoqG9lZtiRRn8tvexd0hcCJux6p9OnF0xKJDTInxfU9C8inrLvY3D4WyuWSeeCQq0RdfXJiJfxP9MJpmnefIbnTeBrLksk0gtH+2PGGvnWp0cxafYGkhPFpsPgkbDzLZh69ylPDwzy56rMRF7ZUMjP5o8jYkCADSFFpF/Z95G1d0xwuN1JvFtyFux4A5xOcGg/dfEO+pcofUp2fjXBAQ6iG3Jh8CgICLE7khzPMCD9Kms+en3nC37cMm0ILe1OXtt06upmIiIuVV8Oh7ZoWll3JE+F5hqr6V/ES6iQkT5lc0EVE5MicZRtV3+Mtxp/DZhOyHmr06fTE8PJSInk+bUHcTrV9C8ibrT/E+s4UoXMGR1r+FefjHgPFTLSZ7S0d7CjuJZzEv2hOl/9Md4qNh2ix8D2N7o85c6ZQ9lf3sDKvRUeDCYi/c6+jyAkytqwV05v8CgIilCfjHgVFTLSZ+QcqqO13cnMsCMrYmnpZe9kGNaozMHPoLbzBQ6vmJBAdGgQz63K82w2Eek/TNNq9B9+Hjj87E7j/RwOSJqsQka8igoZ6TM2H1myd6xfgfWAChnvNe4awLSa/jsR6O/g1umpLNtdRl5Fg2eziUj/ULoD6kthpPaP6bbkqVC6E1r1c1m8gwoZ6TOyC6qJDw8momYXBEdCeKLdkaQrMaOtQnPH612ecuv0VPwdBv9efdCDwUSk39j3kXVUo3/3JU8FswOKs+1OIgKokJE+ZHN+NZmpkdanbPETrClM4r3GXW1tRFfT+epkseHBXDEhgVc2FFDf0u7hcCLS5+39CGLG6kOvnkiaYh01vUy8hAoZ6RMq61vIP9xIRnK4NeytFcu83/hrrOOO0zf917W087qWYhYRV2pthPzVmlbWUwMHQ9RwFTLiNVTISJ+QfWQjzHMG1UFbg/pjfEHUcGuloO1dTy/LTB3EpJRInluVp6WYRcR1Dn4GHa2aVnY2kqdC4QZrsQQRm6mQkT4hu6AaP4dBmnGkn0IjMr5h/DVQvAkOH+jylDtnDtFSzCLiWns/Av9gGDLT7iS+J3kq1JdAbZHdSURUyEjfsDm/mrT4MIIqc8BwQOxYuyNJd4y72jqeZnqZlmIWEZfb97FVxASE2J3E96hPRryIChnxeU6nyZaCajJSIqFku7Vpl345+YbIVOvTvdOsXhbk78et01P5eFcZB7QUs4j0Vk0hVOyGEeqPOStx463RrMINdicRUSEjvm9feT11Le1kpg6C0u2aVuZrxl0DJdugYm+Xp3y+FHOe53KJSN+098iyy2r0Pzv+gVZ/o0ZkxAuokBGft/lIo39mnB9UH4R4Nfr7lHFXAcZpR2Viw4OZNzGBVzcUailmEemdfR9BWCLEpNmdxHclTYFDW6BDP4/FXipkxOdtzq8mPNifYe151gNascy3hCdC6ozTrl4Gny/F/PL6Ag8FE5E+x9kB+z+xVivTXmNnL2kytDdD+S67k0g/p0JGfF52QTWTUiJxlG23HlAh43vGXwPlOVaPUxcyUwcxdeggnll5gLYOpwfDiUifUbQJmmtgpPctu9zS3sHGg1XsKqmlua3D7jinl5hpHYs32ZtD+j1/uwOI9EZDSzu7S2q5+IJRULoDgiO1S7MvGncNLP4BbH3ptFMDvzx3BPf8ewPvbj3EVZlJHgwoIn3Cvo8AA4afb3cSnE6TXSV1fLa3gpV7K1h34DBNRwoYw4DEiBCGxwxkePRAxiVFcFVGEoH+XvL586BhEBQBxZth8hfsTiP9mAoZ8WnbimpwmpCZEgkrt0P8BE0X8EUDB8PIi2Hbq3DRz8Hh1+lpF6TFMio2lCc+3ceVGYkY+v9aRHpi38fWaMKAKNsiNLd18LePc3lxXQGVDa0AjIwN5capKZwzfDCtHU72l9dzoKKB/eUNvLqxkH+tPsgTn+7jZwvGce7oGNuyH+NwQGKGVciI2EiFjPi0zfnVAGQkh0PpTph8u72B5OxNvB72vA95K2H4uZ2e4nAY3Dt3ON99dSuf7innvDGxHg4pIj6rqdpaMnjOt22LsD7vMN9/dSv7Kxq4bFw8F6fHMWtkNPERwV2+xjRNlu0u45dv7+SOZ9dxcXocP52XTurgAR5M3onETFj9GLS3gH+QvVmk3/KSMUqRs5NdUMWw6IEMaimCtgYtvezLRl8OgWGw9eXTnnZlRhLx4cH849P9HgomIn3CgU/B7LAa/T2svqWdh97azvVPrKa1w8nzd0/niduncO2U5NMWMQCGYXBBWhwffGsu378sjc/2VnDRnz/l/z7YTWOrjauGJU0GZ5s1rVvEJipkxGeZpsnm/CMbYR79QapGf98VOADSF8LOt6CtqevT/B3cPXsYq/dXsuXI0tsiIme072Prw5LkqR697Se7y7j0z8v5z5qD3DVrGB9+ay6zR0X3+DpB/n585bwRfPyd87h8fDyPLtvLjf9YQ01TmxtSd4Ma/sULqJARn3WoppmyuhYyUyOtQsZwQOxYu2NJb0y8AVrrYM/i055207QUwoL9+cfyfR4KJiI+zTRh78fWtFW/AI/d9ukV+7nzn+sJCfTj1ftm8tCCdAYE9m5Wf3xEMH+9KZOnvpDFrpJa7vznOnv214pIgQGD1ScjtlIhIz7rWH9MSiSUbofBIyEgxNZM0ktD50BYwhmnl4UFB3D7OUN4f3sJeRUNHgonIj6rci/U5Ht0WtmTy/fx63dzuGJCPO9+YzZThgxy6fUvTo/j0Vsms62whi/+c53np5kZBiROhuJsz95X5DgqZMRnbc6vIsjfQVp8uFXIqD/G9zn8YMJ1kPshNFSe9tQ7Zw0lwOHgyRXqlRGRM9j3sXX0UCHz90/28dv3djFvYgJ/vSmTIP/OV2LsrUvHxfPXmzLZeLCKe/61wfP7zyRmQlkOtDZ69r4iR6iQEZ+VXVDN+KQIAtvroSpP/TF9xcQbwdkOO9847WmxYcFcOyWJVzcWUl7X4qFwIuKT9n4EUcMhapjbb/XYsr38fvEuFk5K5K83ZhDg5963WvMmJvDHGyaxen8l9/5nIy3tHixmkiZbCyiUbPPcPUWOo0JGfFJbh5NtRTXW/jFlOdaDKmT6hrjxEJt+xullAF+aM5y2Dif/WpXn/lwi4pvaWyBvBYy40O23euSjXB7+YDdXZSTypxsm4e/mIuaoqzOT+f01E1m+p5yvvbCJtg6nR+5LQoZ1VMO/2ESFjPikXYfqaGl3kpEaCaVHPgk6zY7w4kMMw2r6L1gLhw+c9tThMaFcmh7Pv1bn2bdyj4h4t/w10Nbo9mllf/solz8t2cM1k5P44w0ZHitijrphagq/umo8S3PKeOSjXM/cNDzB6mtUw7/YRIWM+KTNBVUAZKYOslYsC46A8CSbU4nLTLjeOm575Yyn3n/BSOqa2/nnZ6cvekSkn9q7FBwBMGyO226xeHsJf1yyh2syk3j4ukn4OQy33et0bj9nCNdNSeaxZXvZlF/lmZsmTlYhI7ZRISM+KTu/mpiwIBIjgqFkuzUdybDnF4e4QUSytYLZ1pesZVNPY3xSBJekx/HMygMalRGRU+39CIbMgKAw91y+rJ4HX9nCpJRI/t+1E2wrYo56aEE6CREhfOflLTS1eqBfJjETKnKhudb99xI5iQoZ8UmbC6rJTInEME0o26n+mL5o4g3WkqlFZ557/c2LRlPX3M6zKzUqIyLHqS2Gsh0w8iK3XL6+pZ0v/2cDQf4O/n7rZLetTtYT4cEBPHzdRA5UNPC793Pcf8PETMCEQ1vcfy+Rk6iQEZ9T1dDKgYoGqz+mOg9a67X0cl+UfiX4B0P2C2c+NTGcy8bF8+zKA9Q0alRGRI7Y+5F1dEMhY5om331lC3mVjfztlkwSI71nH7OZI6P54qyh/Gv1QVbklrv3ZomZ1lEN/2IDFTLic7ILqwHITDnSHwNq9O+LgiOsYmbbq9DWdMbTH7hoFHUt7TyzUvvKiMgRe5dazeix6S6/9D+W7+f97SX84LI0Zo6Idvn1e+v7l6UxImYg331lq3s/4Bk4GCJT1ScjtlAhIz4nO78ahwETkyOsQsZwQMxYu2OJO2TcCi01kPPOGU8dmxDO5ePjefazPKobWz0QTkS8Wkc77F8GIy90eQ/lZ3sr+MNia8PLe+a4f2+asxEc4MefbsigvL6Fn7+9w703U8O/2ESFjPiczQXVjI4LY2CQv7UJV9QICBxgdyxxh6FzrE/6sp/v1ukPXDSK+pZ2nlGvjIgUbYDmGhh5sUsvW1zdxNf/t5mRsaH84dqJGF680MyklEjuP38kb2wu4r1th9x3o8RMa2PqxsPuu4dIJ1TIiE9xOk22FFSTmRppPVC6Q/0xfZnDARm3wf5PoergGU9Piw9n3oQE/qlRGRHZuxQMPxh+nssuaZom3311Cy1tHTxx2xTrAzUvd/8FI5mQFMFDb+2goaXdPTc51iejURnxLBUy4lMOVDZQ09Rm9ce01EHVAfXH9HUZN1vHLf/r1ukPXDSKhtZ2nlqhXhmRfm3vUkieCiGRLrvkC2vz+WxvJT+el87wmFCXXdedAvwc/HzhOCrqW9w3Wp2YYR3V8C8epkJGfEp2fjWAtWJZ2ZFlJbX0ct8WmQrDz4XNL4DTecbTR8eFMW9CAs99lsfhBo3KiPRL9eXW6IALVysrONzIb9/LYc6oaG6eluKy63rClCGDuGxcPP/4dB+V9S2uv0FwBAweCcXZrr+2yGmokBGfsrmgirAgf0bGhFr9MaCpZf1B5u1Qkw95y7t1+gMXjqKxrYN/fLrPzcFExCvt+9g6jnJNIeN0mnzv1a04DIPfeXlfTFe+e9kYmtud/O3jve65gRr+xQYqZMSnZBdUMzElAofDsPpjgiIgwrc+GZOzkDbP+sRv85n3lAEYFRfGNZnJ/HNVHoVVjW4OJyJeZ+9SGBAN8ZNccrnn1x5k9f5Kfjp/LEletF9MT4yICeWGrBReWHuQg5UNrr9BYibUFkFdqeuvLdIFFTLiM5paO8g5VGf1xwCUbrdGY3zwkzHpoYAQGH8d5CyCpupuveTBS0djAP/3wW63RhMRL+N0wr6PrGWXHb1/m3OwsoH/994uzh0dww1Zvv3B2TcvGoWfw+CPH+5x/cXV8C82UCEjPmNbUQ0dTpOMlEjrF1XpTjX69yeZt0F7M2x/rVunJ0SEcPfsYbyZXcy2who3hxMRr3EoGxorXdIf43SafPfVrfj7Gfzu2gk+OaXseHHhwdwzeziLtrjh52LCRGtfNzX8iwepkBGfkV1QBRxp9K8+CK116o/pTxIzIXYcbO7enjIA9503gqiBgfz2vRxM03RjOBHxGnuXAgaMuKDXl/rX6jzWHTjMT+enkxDhm1PKTnbvucMZNCCA3y/e5doLBw6EmDSNyIhHqZARn7E5v5rUqAFEhwZZ/TEAcRPsDSWeYxiQeav1aV/pzm69JDw4gG9eNIrV+ytZtrvMzQFFxCvsXWp98DEwuleXKaxq5A+Ld3P+mBiun5LsonD2Cw8O4P4LRrFybwUrcstde/HETKuQ0QdH4iEqZMRnZBdUW9PKwOqPwYDYNDsjiadNvBEcAbD5P91+yc3TUhkePZD/994u2jvOvHyziPiwpiooXO+SaWW/fNv6wOTXV/v+lLKT3XZOKsmDQvjd+7twOl1YdCRmQkM51BS67poip6FCRnxCSU0zh2qayUyNtB4o3Q5Rw62hbOk/BkZbK5hl/xfamrr1kgA/B9+7LI3csnpe3qBfriJ92v5PwHT2upBZtquMD3eW8vULR/rsKmWnE+Tvx4OXjGFHcS3vbjvkugsnTraOml4mHqJCRnzCsf6YYyMyO9To319NvRuaq2HHG91+yaXj4sgaMog/LdlDQ0u7+7KJiL1yl1hLtSdNOetLNLd18PO3dzAiZiD3zB7uwnDeZeGkREbEDOSJT/e5rocwfrw1aq6Gf/GQbhUyhmFcZhjGbsMw9hqG8YNOnk81DGOZYRibDcPYahjGFa6PKv3Z5vxqAv0cpCeGQ0s9HD4AcSpk+qWhc6wdpDc82+2XGIbBj+aNpaK+hSeX73djOBGxjdMJuR/CyIvBz/+sL/PEp/s4WNnIL68cT6B/3/281+Ew+NKc4ewormX1vkrXXNQ/COLSNSIjHnPG/0INw/ADHgMuB9KBmw3DSD/ptJ8AL5ummQncBDzu6qDSv20uqCY9MZwgfz8oywFMFTL9lWFA1l3WPPhDW7v9ssmpg5g3MYEnl+/nUE33pqWJiA8p3mz1Z4y+9KwvcbCygcc/2cf8iQnMGtm7xQJ8wVWZSUSHBvHkChd+wKOGf/Gg7nzUMA3Ya5rmftM0W4EXgStPOscEwo/8OQIodl1E6e/aO5xsK6w5rj9mm3XU0sv916SbwT+4R6MyAD+4LA2nafLrd3LcFExEbJP7gbWPyVn2x5imyc8X7SDAYfCTeSd/Xts3BQf4cefMIXyyu5zdJXWuuWjiZGiugcMa/Rb3604hkwQUHPf3wiOPHe/nwG2GYRQC7wFfd0k6EWB3aR1NbR0n9scEhUNkqq25xEYDomDcNbDtFWjp/i/flKgB3H/+SN7ddojle1y87KiI2GvPYkieZv18OAtLdpaybHc537p4NPERwS4O571unT6EkAA/nnLVqExipnXU9DLxAFdN/rwZeM40zWTgCuA/hmGccm3DMO41DGODYRgbysv1JkK6Z3N+NWBNDQKgZLs1GtPHlsOUHpp6N7TWw9aXe/Sye88dzrDogfxs0Q5a2jvcFE5EPKr2EBzactbTyppaO/jF2zsZHRfKHTOHujablxs0MJAbspJ5K7uI0trm3l8wdqw1Yq5CRjygO4VMEZBy3N+Tjzx2vLuBlwFM01wNBAOnTC41TfNJ0zSzTNPMiomJObvE0u9kF1QzeGAgyYNCrDm3pTvUHyPWqkTxE6zpZT2Yix3k78cvFo7jQEUDT36qqQ8ifULuh9bxLAuZxz/ZS1F1E7+6cjwBfn23wb8rd80eRofT5LlVeb2/mF+A9bNZhYx4QHf+a10PjDIMY5hhGIFYzfyLTjonH7gQwDCMsViFjIZcxCU251eRmRppbUhWfRBa69QfI583/Zdutxr/e2Du6BjmTUjg0WV7KTjc6KaAIuIxuR9CRArE9ry3pbi6iSeX72fhpESmDx/shnDeb8jggVw2Pp4X1hyk3hVL1CdmWiNkTo16i3udsZAxTbMduB/4AMjBWp1sh2EYvzQMY+GR074DfMkwjC3A/4A7TZctSi79WU1TG/vKG07sjwGNyIhlwvUQGNbjpn+An8wfi5/D4OeLdrghmIh4THsL7FsGoy45qynHD3+wGxP43mVjXJ/Nh3xpznBqm9t5eX3BmU8+k8TJ1tTfitzeX0vkNLo1fmqa5numaY42TXOEaZq/OfLYQ6ZpLjry552mac4yTXOSaZoZpml+6M7Q0n9sKagGIPP4/hgMaw6uSFAYTLwBtr8OjYd79NKEiBC+ddFoPtpVxpKdpW4KKCJul7cS2hpg9GU9funWwmre2FzEPbOHkTxogBvC+Y7M1EFMGxrFMysP0N7h7N3F1PAvHtL/JoKKT8kuqMYwYGJyhPVA6XaIGgZBofYGE++RdRd0tED2f3v80jtnDWV0XCg/X7SDplZNgRDxSXs+AP8QGDanRy8zjyzFHh0ayFfOG+GmcL7lS3OHU1TdxHvbS3p3oehREDAQije5JphIF1TIiFfbnF/FqNhQwoIDrAfU6C8nix8PKdNhwzPWzt49EODn4FdXjqeouolHPtYUCBGfY5rWssvDz4WAkB699IMdpazLO8w3Lxr9+e+Yfu7CtFiGRw/kqeX76VWHgMMPEjM0IiNup0JGvJZpmmQXVH/eH9PaYG2wpUJGTjbtXuvfxt4lPX7p9OGDuW5KMk8u38+2who3hBMRt6nYYy0CM+qSHr2std3J797PYVRsKDdNTTnzC/oJh8PgrtnD2FZUw+YjU7vPWmImlGyDjjaXZBPpjAoZ8VoHKxupamz7vD+mLAcwrU/gRY6XfiWEJcCav5/Vy386P53o0EAefGWL9pYR8SV7PrCOPVx2+T9rDpJX2ciP5o3Fvx8ut3w6V2UmMTDQj/+tze/dhRIzob35yO9uEffQf73itTYXVAF8PiJTss06aullOZlfAEy9B/YvO6tfmhEhAfy/ayawu7SOv3201w0BRcQt9nxgjdJHJHf7JdWNrTzyUS5zRkVz3mjtaXey0CB/FmYk8fbWYmqaejGaooZ/8QAVMuK1svOrGRDox+i4MOuB0h3WUrsRqfYGE+805YvWbtJnOSpzQVoc105O5u+f7mNrYbVrs4mI6zVVQ/7qHo/GPPLRXuqa2/jxvLHW/mRyilumpdLc5uSt7JP3P++BqOEQHKGGf3ErFTLitTYXVDMxOQI/x5FfNKXbrdEYh/7ZSicGDraWYt76Uo+XYj7qoQXWFLPvvrJVU8xEvN2+j8DsgFHdL2QOVDTw79V53Dg1hbT4cDeG820TkiOYkBTBf9fmn33Tv2FYozIakRE30jtC8UrNbR3sLK79vD/GNI+sWKZpZXIa079izcne+M+zenlESAC/u2aippiJ+II9H0JIFCRndfslv3s/hyB/B9+6eLQbg/UNN09LZVdJXe+a/hMzoXQntDW7LJfI8VTIiFfaUVxDu9P8vD+mOh9aatXoL6cXlw7Dz4N1T5/1Sjnnp8Vy3RRNMRPxah3tkPuBtVqZw69bL1m7v5IPdpTylfNGEBsW7OaAvm9hRiIDA/34b2+a/hMng7PN+iBSxA1UyIhX2pxfDUDm0ULm6A9BLb0sZzL9K1BXDDvfOutLaBUzES+XvwqaqiBtXrdOdzpNfv1uDgkRwdw9e7ibw/UNR5v+3+lN0/+xhn/1yYh7qJARr7S5oJqkyBBiw498ala63TrGptsXSnzDqEusJtOzbPqHz6eY7Smt5w+Ld7swnIi4RM471uIeIy/s1ulvbSliW1EN37tsDCGB3RvBEbh1ei+b/iOSYUA0FGe7NJfIUSpkxCtl51eTkRr5+QOl22HQMAgKtS2T+AiHwxqVKdoABevP+jLnp8XyhRlDeGblAZbtKnNhQBHpFdOEXe/CiAshcOAZT29q7eAPi3czMTmCKycleSBg3zE+qZdN/4YBSZM1IiNuo0JGvE5ZXTNF1U2fTysDKNmu/hjpvoybISgc1p79qAzAj64YS1p8GN95ZQultWpWFfEKh7KhtrDb08qeWbmfQzXN/PiKsTgcWm65p26Z3sum/8RMKN8FrQ0uzSUCKmTEC2Uf7Y85OiLT2gCH96s/RrovKAwmf8Hqk6kpPOvLBAf48egtk2lq7eBbL2XT4TzLZUhFxHVy3gHDD8ZcfsZTy+qaefyTfVw6Lo7pwwd7IFzfs2BSL5v+EzPBdH6+qbWIC6mQEa+zKb+aAD+DcYkR1gNluwBTSy9Lz0z/sjUFZfXjvbrMyNhQfrFwHKv2VfL3T7Qks4jtdr0DQ2bCgKgznvrnJXto63Dyg8vHeiBY3xQa5M+Vmb1o+j/a8F+k6WXieipkxOtsyq8iPTGC4IAjDZmlRz7F0YiM9ERkKky4DjY+d9YbZB51fVYyCyYl8ueluWw82LtriUgvVOy1pimlzT/jqbtKanlpfQG3nzOUYdFn7qWRrt0yzWr6f3PzWTT9h8VDWKI2xhS3UCEjXqWtw8nWwmomn9DovwMCQyFyiG25xEfNegDaGmDdU726jGEY/Obq8SRFhvCN/2VT03iWS5GKSO/sesc6nqE/xjRNfvNuDmHBAXzjwpEeCNa3jU+KYHxSOK9sLDi7C6jhX9xEhYx4lZxDtTS3OZkyZNDnD5Zst6aVOfTPVXoobhyMvgzWPtHrRtPw4AAeuTmT0tpmvvfalrNbwUdEemfXO5CQAZEppz3tkz3lrMit4BsXjiJyQKBnsvVx105OZntRLbtL6nr+4sQMqNwLzTUuzyX9m94ZilfZdLAKgMmpRwoZ07RGZNQfI2dr9reg6TBsfr7Xl8pIieQHl6fxwY5Snvh0vwvCiUi31R6CwvUw9vTTyto7nPz23RyGDh7A7edoJN9VFkxKxN9h8Prms1hA5WifzKEtrg0l/Z4KGfEqm/KriQ8PJjEyxHqgpgBaalTIyNlLPQdSZ8Cqv0FH76eE3T17GAsmJfKHD3bx6Z5yFwQUkW7Z/a51PEN/zIvrC8gtq+cHl48l0F9vc1wlOjSI88bE8Obmop6v4Jg42Tqq4V9cTP+Fi1fZlF/F5CGRnz9waKt1jJ9kSx7pI2Z90yqKt7/W60sZhsHvr53AmLgwvv7fTRys1N4IIh6R8w5EjYCYtC5PqWtu489L9jBtWBSXjovzYLj+4ZrJyZTWtrBqX0XPXjggyupzVcO/uJgKGfEaZbXNFFY1fT6tDKBkKxgOjchI74y6BGLTYeVfwOns9eUGBPrz5O1ZGIbBl/+zkcbW9t5nFJGuNVVD3gprWpnR9aaWj3+yj8qGVn4ybyzGac6Ts3NBWizhwf68vuksVi9LmqxCRlxOhYx4jU35Vn9M5vGFzKGtMHgkBA6wKZX0CQ6HNSpTngO5H7jkkqmDB/C3mzPZU1rH917dquZ/EXfK/RCc7ZC2oMtTCqsaeWblAa7JTGJicqTnsvUjwQF+zJ+UyOLtJdS39PADnMRMqD4IDZXuCSf9kgoZ8Rqb8qsJ9HMwPin88wdLtkL8RPtCSd8x/hqISIWVf3bZJeeOjuF7l6XxztZDPLlczf8ibpPzNoTGQ9KULk/5w+LdGMCDl47xXK5+6NrJSTS1dbB4e0nPXnis4V+jMuI6KmTEa2w6WMX4pHCC/I9shNlQCbVFkKBCRlzALwBm3g8Fa+HgKpdd9stzhzNvYgK/X6zmfxG3aGuCvR9B2hVdLsO/Ob+KRVuKuXfu8M8XixG3mJw6iCGDB/D6ph6uXpaQYR01vUxcSIWMeIXWdidbi2pO7Y8BjciI62TeDgOi4dPfu+yShmHw8HUTGRMfztde2MSuklqXXVtEgNwl1sa2YzufVmaaJr9+N4fo0CC+fO4ID4frfwzD4JrMZFbvr6Souqn7LwwOh8GjoEiFjLiOChnxCjsP1dLa7mTykE4KmQStWCYuEjgAZj0A+z+Bg6tddtkBgf48e2cWA4P8uOuf6ymtbXbZtUX6ve2vwcAYGDq306ff21bCxoNVPHjJaEKD/D0crn+6OjMJ04Q3N/ew6T8xUyMy4lIqZMQrnLIRJliN/uHJ1rKNIq4y9W7rTdEn/8+ll02ICOGZO6ZS3dTG3f9aT0NPG2FF5FQtdbDnA0i/EvxOLVKa2zr4f+/nkBYfxvVZKTYE7J9SBw9g2tAoXt9U2LOFTpImQ10x1PWwv0akCypkxCtsyq8iMSKY+Ijgzx8s2ar+GHG9wIHWCmYHPnVprwzA+KQIHrtlMjuLa3ngxc093zRORE60ezG0N8H4azt9+pmVByisauKhBen4ObTcsiddMzmJfeUNbC2s6f6Ljjb8a1RGXESFjHiFTQerTpxW1toAFbnqjxH3yLoLBsa6fFQG4Py0WH6xcBxLc8r41Ts7XX59kX5l+2sQlggp55zyVFltM48v28sl6XHMHBFtQ7j+7YqJCQT6O3rW9B8/0dobToWMuIgKGbFdSU0zxTXNJ04rK90BmBqREfcIHACzvwkHlkPeZy6//O0zhnLP7GE8tyqPZ1cecPn1RfqFpirYu9RaOr2T1cr+78PdtHY4+dEVY20IJ+HBAVySHseiLcW0tndzo+HAARAzVoWMuIwKGbHd0Y0wTxiRObTFOsZPsCGR9AtZd0FonFtGZQB+dMVYLhsXz6/e3cnbW4rdcg+RPm3Xu+BsswqZk2wvquGVjYV8cdYwhkYPtCGcgDW9rKqxjeU9WXo+MROKNoE2ERYXUCEjttt0sIogfwfpCcdvhLkNgiMhQs2b4iYBITD7W5C3Ag6scPnlHQ6Dv9yUwdShUXz75WztMSPSU9tfg0FDIXHyCQ+bpskv39lJ1IBA7r9gpD3ZBIA5o2IYNCCART35sCYpExoroKaH+9CIdEKFjNhuU34VE5IiCPQ/7p/j0UZ/Q82b4kZT7rR2C//kd265fHCAH0/fkcXouDDu+89GNh487Jb7iPQ59eWw/1MYd80pvwfe317CugOH+c4lYwgPDrApoAAE+Dm4fEICS3aW0tjazZUajzX8b3JfMOk3VMiIrVraO9heVHvitLKONijdqUZ/cb+AEJjzbTi40uqXcYPw4AD+ddc04iOC+eI/12vDTJHuyHkLzI5TVitrbuvgt+9Zyy3fOFUj9t5g4aREmto6WJpT1r0XxI0HR4D6ZMQlVMiIrbYX1dLa4Tyx0b9iD3S0aCNM8YzJd0BYAnz8G7fN2Y4ODeI/d09jQKA/tz+zjvzKRrfcR6TP2P46RI+BuHEnPPzsZ0eWW56v5Za9xbShUcSHB7Mou5vTy/yDrP9fizQiI72nQkZstflYo3/k5w8e2modNSIjnhAQDOd+DwrWwO733Xab5EED+M/d02jrcHLbM2spq212271EfFpNkbXH0/hrT5hWVlrbzGMf7+Xi9DhmjtRyy97C4TCYPzGBT/eUUdPY1r0XJU2xRmSc3VztTKQLKmTEVpvyq0geFEJs2EkbYfqHQPQo+4JJ/5L5BRg8Epb+HDq6Oc/7LIyKC+O5L06jor6FW59eS2V9i9vuJeKzdr4JmKesVvabd3Noc5r8dF66LbGkawszEmnrMFm841D3XpA0BVpqoXKve4NJn6dCRmxjmiabDlafOK0MrBGZuHHg8LMnmPQ/fv5w4c+gYjds+a9bb5WREskzd0yloKqRW59ey+GGVrfeT8TnbH/NGpE/7sOs1fsqWbSlmPvOHUHq4AE2hpPOTEiKYOjgAd1fvSw5yzoWbXRfKOkXVMiIbYqqmyipbWbK8Y3+pmktvayNMMXTxi6A5Kmw7LfQ6t4elhkjBvPMHVM5UNHAbU+vpbpRxYwIAIcPWG9ujxuNaetw8rNF20keFMJXzxthYzjpimEYLJyUyOp9lZTVdWPa7OBREBgGRRvcH076NBUyYpsNeVZ/zNShUZ8/WJUHLTXaCFM8zzDg4l9C3SFY+3e3327WyGie+kIWe8vruf2ZddQ0dXNuuUhftu1V6zju80LmX6vy2FNaz0Pz0wkO0Ei9t1qYkYjThHe3dmN6mcNh7SejERnpJRUyYpv1eYcJC/JnTHzY5w+WbLOO8VqxTGwwZCaMvhxW/gUa3b/ny9zRMfzjtinsLqnjC8+uo7ZZxYz0Y6YJ2S/A0DkwaAgAZbXN/GVpLueNieHi9DibA8rpjIwNY2xCOG91d/WypClQsh3atPCJnD0VMmKbDXlVTB4y6MQlNEu2guEHcWrmFJtc9DNorYfl/+eR252fFsvjt05mZ3ENdzy7jjoVM9JfHVwFVQcg49ZjD/32vRxa2538fME4DG2Q7PUWTkoku6C6e0vMJ2WBs+3zDzBFzoIKGbFFTWMbu0vrmDq0k0b/6NHWRoUidogdCxm3wPqnoOqgR255UXocf7t5MtsKa7jtmXXdX8JUpC/Z/LzVN5G+EIC1+yt5M7uYe+cOZ2j0QJvDSXcsmJQAwNtbuzEqkzTFOmp6mfSCChmxxcZ8a9pO1vH9MWCNyKjRX+x23o/AcMCy33jslpeNj+eJ26aQU1zLzU+t0Wpm0r+01FnLLo+/GgIH0t7h5GeLdpAUGcLXzh9pdzrppuRBA5gyZFD3NscMT4CwRBUy0isqZMQW6/OqCPAzmJQc+fmD9eVWo7U2whS7RSTB9Ptg60se/SV7UXocT35hCvvK67npydWU12mfGekndrwJbY2QcRsA/1p9kF0ldfx0/lhCAtXg70sWTkpkd2kdu0vqznxy0mStXCa90q1CxjCMywzD2G0Yxl7DMH7QxTk3GIax0zCMHYZhuHcjBvF5G/IOMz4p4sRfUCVbrKNGZMQbzPkODIyF97/v0d2nzxsTyz/vnErB4SZufHI1JTVqhJV+YPPz1pK8KdMorGrkjx/u5rwxMVw6Lt7uZNJDV0xIwGHAoi1FZz45OQsO7/fI4irSN52xkDEMww94DLgcSAduNgwj/aRzRgE/BGaZpjkO+Kbro0pf0dzWwZaCmhOXXQY4dKSQ0dLL4g2Cw+HiX0DhemtkxoNmjozm33dPo6y2hRv+sZrCKvfuayNiq4q9ULAGMm/FBH7y5nYAfn3VeDX4+6CYsCBmjYzm7S2HME3z9Ccf7ZMp3uT+YNIndWdEZhqw1zTN/aZptgIvAleedM6XgMdM06wCME2zzLUxpS/ZVlRDa4eTrCEnNfoXb4ao4RAyqPMXinjaxJusX7RLf2bN4fegqUOjeP6e6VQ3tnLDE6vZV17v0fuLeEz2C1ZP2sSbWLSlmE92l/OdS8aQPGiA3cnkLC2YmEj+4Ua2FdWc/sSEDMCAQvXJyNnpTiGTBBQc9/fCI48dbzQw2jCMzwzDWGMYxmWdXcgwjHsNw9hgGMaG8vLys0ssPm99njWEPOWUQiYbEjM9H0ikKw4HXP4w1JfC8oc9fvuMlEj+d+85tHY4uf6J1Ww/05sCEV/j7IAt/4ORF1PlN5hfvr2TSckR3DlzqN3JpBcuHRdPgJ/BO2faHDM4HGLS1PAvZ81Vzf7+wCjgPOBm4CnDMCJPPsk0zSdN08wyTTMrJibGRbcWX7Mhr4oRMQMZHBr0+YP15VBToEJGvE/yFKsBefXj1hQYDxuXGMEr980kJMCPm55cw5r9lR7PIOI2+z62FnnJvJXfvJdDTVMbv7t24on7i4nPiRgQwJxRMby79RBOZzemlxVttDZEFemh7hQyRUDKcX9PPvLY8QqBRaZptpmmeQDYg1XYiJzA6TTZkHf41P6Y4s3WUYWMeKMLHwL/YPjgR7bcflj0QF77ykwSIoL5wrPrWLKz1JYcIi63+XkIieIzx1Re3VjIvXOHMzYh3O5U4gLzJyZQVN3E5oKq05+YNBkaK6DaM/t2Sd/SnUJmPTDKMIxhhmEEAjcBi046502s0RgMw4jGmmq233Uxpa/ILauntrn91P1jijcDBiRMsiWXyGmFxcF534fcD2DPh7ZEiI8I5uUvz2BsQjj3Pb+R1zcV2pJDxGUaD8Pu92gffz0/XLSboYMH8I0L9RloX3FxehyB/g7e3nKG6WXaGFN64YyFjGma7cD9wAdADvCyaZo7DMP4pWEYC4+c9gFQaRjGTmAZ8F3TNDX/QU5xtD9mWmeFTPRoCAqzIZVIN0z7srU87OIfQLs9m1UOGhjIC/dM55zhUXz75S08vUKfF4kP2/YKdLTyr6bZ5B9u5LfXTCA4QHvG9BVhwQGcNzqG97YdouN008vixlkj3kVauUx6rls9MqZpvmea5mjTNEeYpvmbI489ZJrmoiN/Nk3T/LZpmummaU4wTfNFd4YW37Uh7zCxYUGkRIWc+ETxZk0rE+/mHwiX/w4O74PP/mpbjNAgf569cyqXj4/n1+/m8Ot3dp55DrqItzFN2PgcjYPH89tN/tyYlcLMEdF2pxIXmz8pkbK6lmMfYnbKL8CajaERGTkLrmr2F+mW9XlVTB0adeLeALXFUF+iQka838iLYNzV1gpmNjT+HxXk78ejt0zmjhlDeHrlAb75UjYt7R225RHpsbwVULaTRxvOJzYsiB/NG2t3InGDC9NiCQ5w8M7W4tOfmDTFWrm0o80juaTvUCEjHlNc3URRdRNZQzvZPwashj8Rb3fZ761pEO9809ZVdvwcBj9fOI4fXJ7Goi3FfPGf66lr1psA8RFr/0GjXwTPVE/h/66fRERIgN2JxA0GBvlz4dg43t9WQnuHs+sTk6ZAexOU5XgunPQJKmTEYzYctFYu6XTFMsMP4sbbkEqkh8Li4JJfWp8ob37e1iiGYXDfuSP40w2TWHfgMDf8Yw2ltc22ZhI5o+p8zN3v8VzLudw8czSzRmpKWV+2YGIClQ2trNl/mullaviXs6RCRjxmQ95hBgb6kRZ/UkN/8WaIHQuB2sVZfETmFyB1Jnz4E6gvszsN10xO5tk7p5Jf2cA1j69iT2md3ZFEutSy+kk6TFgRsZAfXJ5mdxxxs/PGxDIw0O/008sGDYWQKCja4LFc0jeokBGPWZ9XxeQhg/D3O+6fnWlaK5WoP0Z8icMBC/4KbY2w+Id2pwFg7ugYXvryDFo7nFz791V8trfC7kgip2ptpH39v1jizOIHN12sVcr6geAAPy5Oj2PxjhLauppeZhhHNsbUymXSMypkxCNqm9vYVVJL1pCTppVV50PTYRUy4ntiRsOcB2H7q5C7xO40AIxPiuDNr80iMSKEO55dx8sbCuyOJHKCre8/zUBnLfWT7mZSSqTdccRD5k9MpLqxjZWn+4AlOcvqkWmu9Vww8XkqZMQjNh2swjRhaleN/ipkxBfN/iZEj4F3vg0t9XanASApMoRXvjKDGSMG871Xt/J/H+zGtHFRApGjymqaCNr8NAf8hnHVldfbHUc8aM7oaMKC/XnndJtjJk8FTPXJSI+okBGPWJ93GD+HQUZq5IlPFG8GR4C1IZaIr/EPsqaY1eTDx7+2O80x4cEBPHvnVG6amsKjy/bywItanlns5XSaPPX884zhIAPnfo0Af00p60+C/P24dFw8H+4oobmti59FyVmAAYXrPZpNfJsKGfGINfsPMyEpggGB/ic+UbwZ4sdbbwhFfNGQGTDtXlj7dziw3O40xwT4Ofh/10zgu5eOYdGWYm59ai0V9S12x5J+6rFle8kseZmWgAhiZ95mdxyxwfyJCdS1tLN8T3nnJwRHWAv/FKz1bDDxaSpkxO0aW9vZUlDNjBGDT3zC6bQ2wNK0MvF1F/0CBo+EN74CzTV2pznGMAy+dv5IHr0lk21FNVz56GfsKtH8c/GsVXsreGnpKi7120Dg1DshIMTuSGKDWSOjGTQggHe2nmZ6Wco0KFhvvT8Q6QYVMuJ2Gw9W0e40OWf4SYVM1QFoqVEhI74vcABc/STUHYL3f2B3mlPMn5jIy1+eQVuHk2sfX8VHOaV2R5J+oqy2mW+8mM3Xwj7FYYAx7R67I4lNAvwcXDY+nqU5pTS1djG9LGW69b6gYo9nw4nPUiEjbrd6XyX+DoOsIWr0lz4seQrM+Q5s+S/kvG13mlNMSolk0f2zGRYzkHv+vYGnV+zXIgDiVu0dTr7+v804W+q5wfExxpgrIDLV7lhiowUTE2ls7WDZ7i7230qeZh01vUy6SYWMuN2a/ZVMTI5gYFAn/TH+wRAz1p5gIq527vcgYRK8/YBXbJR5sviIYF7+8gwuHx/Pr9/N4QevbaO1XVM4xD3+vHQPaw8c5rkJ2/FrroJZD9gdSWw2ffhgokODut4cc/AIa2PMgnWeDSY+S4WMuFVDSztbC2tOnVYG1sZX8RPBz//U50R8kV+ANcWspR4WfcPa8NXLDAj059GbJ/ONC0by0oYCbn5qDWV1zXbHkj5m2a4yHlu2j9uzYpl48F8w7Fyr/0H6NT+HwRUT4vl4VxkNLe2nnmAY1vQyjchIN6mQEbfacKQ/5tRG/w44tEXTyqTviU2Di34Oe96Hzc/bnaZTDofBty8Zw6O3ZLKzuJaFf/uMLQXVdseSPqKwqpFvvZxNekI4DyVugIYya7RSBKtnr7nNydKuevVSpkJlLjQe9mww8UkqZMSt1uy3+mOmnNwfU5ELbQ2QNNmeYCLuNP0+GDoH3v8+lHtv0+r8iYm89pWZ+PsZXP+P1by6sdDuSOLj6prbuPu5DXQ4TR67aRwBqx+B1JkwdLbd0cRLZA0ZRHx4cNerl6VMt47aT0a6QYWMuNXqfZVMSonsfP8Y0IiM9E0OB1zzJAQEwyt3QGuj3Ym6lJ4YzqL7Z5M1ZBAPvrKFX7y9g/YO9c1Iz3U4Tb7xv83sLa/n77dOYVjBW1BXDOd+1+5o4kUcDoMrJiTw6e5yapvbTj0hcTIYfppeJt2iQkbcpr6lnW1FNczorD+meBMEhlp7b4j0ReGJcM1TUJYD73v3G7mogYH8+65pfHHWUP75WR63P7OO8jptnik985t3c1i2u5xfLBzH7OERsPJPkDQFhp9vdzTxMvMnJdDa4WTJjk6mlwUOgPgJaviXblEhI26zIe8wHZ3tHwNQtBESMsDh5/FcIh4z8kKY+6DVK5P9X7vTnJa/n4OfLRjHH6+fxKb8Kub/bQUbD2qOunTP82sO8uxnB7hr1jBuO2cIbH0ZqvNh7vesBm6R42SmRJIUGcLbXa1eljLdep/Q0cmCACLHUSEjbrN6fyUBfp30x7Q1WY3+KVPtCSbiSef90OqXeefb1uiMl7t2SjJvfHUWwQF+3PiPNTz32QHtNyOntSK3nJ8t2sEFabH8eN5YazGXFX+0VqUcfand8cQLGYbB/IkJrMytoKqh9dQTUqZBWyOUbvd8OPEpKmTEbdbsP0xGSiQhgSeNuhRng7P9842vRPoyhx9c+wwEhcHLd1hLM3u5o30z542J4edv7+SBF7M7XypV+r29ZXV89YVNjIoN5ZGbM/FzGLD9dTi8D+Z+V6Mx0qX5ExNpd5p8sKPk1CePLtWthn85AxUy4hZ1zW1s76o/pvDIvNdkjchIPxEWB9c+bS0p+u63vXJ/mZNFhATw5O1ZfPfSMbyztZirHvuMvWV1dscSL1JS08wXn1tPkL8fT9+RRWiQPzidsOL/IDYd0ubbHVG82PikcIYOHtD56mURKRCWoIZ/OSMVMuIWG/Kquu6PKVgHg4ZBaIzng4nYZfi5cO4PYOtLsO5Ju9N0i8Nh8LXzR/Kfu6dzuKGVBX/7TEs0CwCV9S3c+vQaqhraeOaOLJIHDbCe2P4alO+COd+xVu8T6YI1vSyRVfsqqKhvOflJa1RGhYycgX7KiFus2V9JoJ+DySf3x5imNVSsHZ6lP5r7XRgzDxb/APZ+ZHeabps1Mpr3HpjDpJQIHnxlC99+SVPN+rOapjZuf2YdRdVNPHNHFpNSIq0n2ppg6c8hYRKMu8bOiOIj5k9KwGnC+9s7mV6WPM1aMKKuk+dEjlAhI26xen8lGamRBAec1B9TnQ/1pZpWJv3T0f1lYtPhlS9aG8P6iLjwYF645xy+edEo3sguYsGjK9lZXGt3LPGwhpZ27vznOvaW1fOP27OYfvyo++rHoLYQLv2tRmOkW8bEhTEyNpR3tnSyetnRjTG1DLOchn7SiMvVHumP6XRa2dHGPY3ISH8VFAo3/w/8AuC/N0Kj7yxx7Ocw+OZFo3nhnunUN7dz1eOf8Z81B7WqWT/R3NbBPf/awNbCGh65OZNzRx83PbiuFFb+2eqLGTrbvpDiU46uXrYu7zCltc0nPpkwEfyCNL1MTkuFjLjc+gOHcZp03uhfsBYCBkLsOM8HE/EWkalw0wtQUwCv3Akdnexu7cVmjrCmmp0zfDA/fXM7X/r3hlPnuEuf0tru5KsvbGLNgUr+eP0kLhsff+IJy34N7S1w8S/tCSg+a/7EREwT3tt2UtO/fxAkZmhERk5LhYy43Jr9lQT6O8hMjTz1yYJ1kDQZ/Pw9nkvEq6SeA/P/Agc+hcU/tDtNj0WHBvHcnVP56fx0ludWcNlflvPxrk526Raf19zWwVdf2MjHu8r4zVUTuCoz6cQTSrbBpv/A9C/D4BH2hBSfNTI2lLT4sM5XL0uZBoeyrSJZpBMqZMTl1uw/TGZKJ/0xrUc2t9K0MhFL5q0w8+uw/ilY84TdaXrM4TC4e/YwFt0/i+jQIO56bgM/eXMbTa0ddkcTF6ltbuMLz6zjo11l/Pqq8dwyPfXEE0wTPvgRhAyCuQ/aE1J83oJJiWw8WEVRddOJT6RMh45Wa/85kU6okBGXqmlqY0dxDTNGdDKtrHizNsIUOdlFv7D6ChZ/H7a+Yneas5IWH86bX5vFl+YM4/k1+cz72wq2FFTbHUt6qayumRv/sYbNBVX87eZMbjtnyKkn7VkMB5bDeT+0ihmRszB/YgIA7249qek/5RzrmL/Kw4nEV6iQEZdava+y6/4YbYQpciqHH1z7DAydA2/eB7lL7E50VoID/PjxvHReuGc6jS0dXP34Z/zu/V00t2l0xhcVHG7k+idWk1fRwNN3TGX+xMRTT+pogw9/AoNHQdYXPR9S+owhgwcyISni1OlloTEQPRoOqpCRzqmQEZdakVvOwEC/U/ePAShYD1EjYGAnRY5IfxYQDDf9F+LGwUu3Q77vrtIza2Q0H3xrLtdPSeGJT/cx75EVbDxYZXcs6YHdJXVc+/dVVDe28cKXpp+4Otnx1j0FlXvhkl9bq/CJ9MKCSQlsLazhYGXDiU8MmQX5a8CpD0XkVCpkxKVW5FYwY0Q0AX4n/dMyTWtERv0xIp0LDodbX4PwRPjv9VC60+5EZy0iJIDfXzeRf981jeY2J9c9sYpfv7NTvTM+YPmecq5/YhWGAa/cN4PJqV1MFzt8AD7+FYy8GEZf6tmQ0ifNOzLqd8qozNDZ0FILJVttSCXeToWMuExeRQP5hxuZOzr61Cer8qChXNPKRE4nNAZufwMCBsB/rrb+u/Fhc0fHsPibc7hlWipPrzzA5X9dzsrcCrtjSSdM0+SJT/dx5z/XkRgZwqv3zWR0XFjnJzudsOjr4PCHBX8Bw/BoVumbkiJDmJwaeWohM2SmddT0MumEChlxmRW55QDMHdXJNISj68BrREbk9AYNsYqZ9mb410KoOmh3ol4JCw7gN1dP4L/3TMcEbntmLd/432bKTt78TmzT2NrO/f/bzO/e38XlExJ4/aszSYka0PULNj4LeSusKWURyZ4LKn3e/ImJ5ByqZV95/ecPhifCoGGQ95l9wcRrqZARl1meW0FKVAhDBnfyC7BwHQSGQmy654OJ+JrYsVYx01wNz82Dw/vtTtRrM0dG88E35/KNC0exeHsJF/7xU/61Ko8Op2l3tH4tv7KRax5fxfvbDvGDy9N49OZMBgSeZp+v6nxY8jMYfj5M/oLngkq/MG9iAoYB72w5eXrZLGvlMqfTnmDitVTIiEu0dThZva+SOaNiMDqbZnB0I0yH36nPicipkibDHW9DawP8cx5U7LU7Ua8FB/jx7YtHs/ibc5iUEsnPFu3gysdWkq2lmm2xbFcZCx5dyaGaZp774jTuO3dE5z+/jzJNWPQN688LH9GUMnG5uPBgpg6N4p2Tl2EeMhuaqqDMd3sHxT1UyIhLbM6vpr6lnbmjOumPaW2A0h3WxlYi0n0Jk+DOd6wN4Z67Asp3253IJYbHhPKfu6fxyM2ZlNa2cNVjn/HNFzefuhmeuEVDSzs/emMbX3xuPQkRwbx9/2zmdrUy2fE2/Rv2L4OLfwGRqWc+X+QsLJiYQG5ZPbtL6j5/UH0y0gUVMuISK3LL8XMYzBjRSSFTtAnMDm2EKXI24sbBne9af/7nFdaHAn2AYRgsnJTIx985l6+dP4L3tpdwwf99wsMf7KK+pd3ueH3W+rzDXP7XFfxvXT73zh3Om1+bRWpn04FPVlNk7RkzdA5Mucv9QaXfumx8Ag6DE0dlBg2BiBQ4uNK+YOKVVMiISyzPrSAjJZKIkE72Eji2EWaWZ0OJ9BWxaXDne+AXaPXM5K+xO5HLhAUH8N1L01j24HlcPj6ex5bt47yHl/Hftfm0d2g+vKs0t3Xw2/dyuOEfqzExeeneGfzoirEEB3Rjuq/TCYvuB2c7LPwbOPTWQdwnJiyIGSMG887WQ5jmcT10Q2ZZIzKm+urkc/ppJL1W1dDK1sJq5nQ2rQysjTAHj4IBUZ4NJtKXRI+EL74HIVHWamY73rA7kUslRYbwl5syefNrsxg6eCA/emMbF/3pU17fVKgFAXpp3YHDLHx0JU8u389NU1N5/4G5TBvWg5/Hy/8A+z6GS38DUcPcF1TkiPkTEzlQ0cCO4trPHxw6y9rGoSLXvmDidVTISK99tq8C04Q5nS27rI0wRVwnahjcsxQSM+GVO+Gzv/a5TyczUiJ55b4ZPHn7FEIC/fn2y1u4+E+f8lZ2kQqaHiqqbuL+/27ihn+spq65nX9+cSr/75oJhAadZlWyk+35ED75HUy6BaZ80X1hRY5z2bh4/B0Gb285bnrZkFnWUdPL5DgqZKTXVuypIDzYn0nJEac+WbEHGish9RzPBxPpiwZEwRfegnFXw5KH4N1vQ0ff6ikxDINLxsXz7tdn88Rtkwn0d/DAi9lc+pflvJVdpClnZ9DY2s6fluzhgv/7hCU7S3ngwlF89J1zOX9MbM8udHg/vH4PxI+H+X/SKmXiMYMGBjJ3dAyLthTjPPoBRtRwCI3XfjJygh58LCNyKtM0WZFbzqyR0fj7dVIX562wjkNnezaYSF8WEAzXPguRQ+Czv0BNIVz3LAR1sRO7j3I4DC4bn8Al6fEs3lHCX5bu4YEXs/nD4t3cOXMoN05LITy4k768fqq9w8miLcU8/MFuDtU0M39iAj+8YixJkSE9v1hrI7z0BcCAG/4DAWdxDZFeuCoziY93lbH2wGFmjBhsFdJDZ8HBz6yRaBXWgkZkpJf2lddTXNPc+bQygLyVEJ5k7corIq7jcFjL4M7/M+z9CJ48H8py7E7lFg6HwRUTElj8wFye/kIWKVEh/Oa9HGb89iN+8fYOCg432h3RVs1tHbyw9iAX/PFTvv3yFqIGBvLyl2fw6C2Tz66IMU1451tQuh2ufVp9MWKLi8fGMTDQjzc3F33+4JBZUHcIqg7YF0y8SrcKGcMwLjMMY7dhGHsNw/jBac671jAM0zAMLU/VTyzfUwHQeaO/aVqFzNDZ+uRExF2y7rKmmjXXwFMXwNZX7E7kNg6HwUXpcbx47wze+fpsLhkXz39WH+Tch5dx93PrWby9hNb2/jPtrKGlnaeW72fuH5bx4ze2M2hAAP+4fQpv3z+7Z838J1v/NGx9Ec77IYy62HWBRXogJNCPy8Yn8N62QzS3dVgPHu2T0fQyOeKMU8sMw/ADHgMuBgqB9YZhLDJNc+dJ54UBDwBr3RFUvNOK3HKGRw8kJaqTfQgq9lgrjGhamYh7DZsD962wFgB4/R5rgY1LfgP+gXYnc5vxSRH8+cYMvn9ZGv9Zk8crGwr5aFcZgwcGclVmEjdkpTAmvm9NtTsqt7SOVzYW8tL6Amqa2pg5YjB/vjGDmSMGY/T2Q6O9S2HxD2HUpTD3u64JLHKWrs5M4rVNhXy8q4wrJiRAzBgYEG1NL5t8u93xxAt0p0dmGrDXNM39AIZhvAhcCew86bxfAb8H9JOvn2hp72DN/sPckJXc+Ql5R1YWOfoJioi4T1g83PE2LP05rH7U2oj2+ucgMsXuZG4VHxHMdy9N41sXjWZFbgUvbyjg36vzeGblASYkRXD5hHguHRfPiJhQu6P2Sm1zG29vKeaVDYVkF1Tj7zC4aGwcXz53OJmpg1xzk4Or4cXbICYNrnlS+8WI7WaMGExsWBBvbC6yChnDgCEzrUJGhO4VMklAwXF/LwSmH3+CYRiTgRTTNN81DEOFTD+xMa+KpraO0/fHhCVaK42IiPv5BVh7faRMgze/Bn+fCZf/Hibd3Oend/r7OTg/LZbz02I53NDKm5uLeDO7iD8s3s0fFu9mZGwol46L45L0eCYmR/R+5MIDahrb+DS3nKU7S/lgRwkt7U5Gx4Xyk3ljuSoziejQINfd7NAW+O8NEJEEt78BIZGuu7bIWfJzGFyZkchzq/Koamhl0MBAa5ZHziKoLujzH9TImfV61TLDMBzAn4A7u3HuvcC9AKmpqb29tdhseW4F/g6Dc0YMPvXJo/0xw8/r82+gRLxO+pUQPxHe/Cq8+RXIeRvm/wXC4uxO5hFRAwO5a/Yw7po9jOLqJpYcKQSe+HQ/jy3bR3RoINOHDWb68CimDxvMqNhQHA77f06Zpsnu0jo+3lXGsl1lbDxYhdOEQQMCuG5KMjdkpbinCKvIhf9cA0HhcPubENrFh1MiNrgqM4mnVhzg3W2HuO2cIdaIDFijMpE32RtObNedQqYIOL7kTT7y2FFhwHjgkyM/XOOBRYZhLDRNc8PxFzJN80ngSYCsrCztbObjPtldxpQhgzrfXK0iFxrK1B8jYpeoYXDnu7D277D0F/D4OdZeIOOutjuZRyVGhnDHzKHcMXMoVQ2tfLSrjM/2VrB2fyXvbjsEWIXP1KGDGJ8YwZj4MNLiw0keFOL24qasrpmtBTVsLaxmS2EN24pqONzQCkB6QjhfPW8k56fFkpESiZ+7slTnw7+vtD5w+sJb+oRbvE56Qjij40J5c3ORVcjEjoPgSDiwAiapkOnvulPIrAdGGYYxDKuAuQm45eiTpmnWAMeWrDIM4xPgwZOLGOlbCg43squkjh9fMbbzE7R/jIj9HA6Y8TUYeRG8cZ+1GMCON+Gy/wfhiXan87hBAwO5bkoy101JxjRNCquaWL2/krX7D7Ph4GE+2FF67NyBgX6MigtjREwo8RFBxIcHExseTHx4MHHhwYQG+xPo5yDAzzhlhKTDadLY2k5jawf1Le2U17VQWNVEYVUjBYet48HKRkpqmwFwGDAqNowL02LJGjqIc0fHEh8R7P7/QepKrCKmtR7ufA+iR7r/niI9ZBgGV2Um8YfFu8mvbCR18AAYfi7sX6b9ZOTMhYxpmu2GYdwPfAD4Ac+aprnDMIxfAhtM01zk7pDifT7KsX7hX5TexVSVg59BWIL6Y0S8QcwYuHsJfPZn+PRha2Wqc78H07/Sp1c2Ox3DMEiJGkBK1ABuyLJGIepb2tlTWsfuEutrV0ktn+2toLy+hQ5n55MIDAMC/RwE+Tvwcxg0tnbQ0sUS0IYB8eHBJA8KYeaIwaQnhjMpJZJxieEMCPTw/tTlu+H566CxEr7wJsSP9+z9RXrgygyrkHkru4ivXzgKRlwAO9+yZn/EjLY7ntioWz85TdN8D3jvpMce6uLc83ofS7zd0pwyRsQMZFj0wFOfPNofM2yuPikR8RZ+/tZyuuOvhcU/giUPwebn4YqHrV42ITTIn8mpg5h80ipgHU6TyvoWSmqbKa1tobS2mcbWdlrbnbQc+Wptd9LudDIg0J8BgX6EBvkf+3N0aBDJg0JIiAwmyN/Ppu/uOAdXwf9uBr9AuPMdSJpsdyKR00qKDGH6sCjeyC7i/gtGYgw/33pi38cqZPo5D38EJH1BbXMba/ZXcvecLnZ7rtwL9aWaVibijaKGwy0vwp4P4P3vWVOL0q+CCx+CwSPsTueV/BwGsUemlvm87a9Z0wwjh8Btr8KgoXYnEumWqzOT+MHr29hWVMPE5CEQNcIqZM65z+5oYiMtEi899unuctqdJheP7WJa2bH+mDmeCyUiPTP6UvjqWjj/x1ZR89g0ePsBqCk682vF95gmfPYIvHoXJE2Buz9UESM+5fIJCQT6OXhj85GfUSMusN5vtLfYG0xspUJGemxpTimDBwZ2vQlb3kr1x4j4goBgq1fmgWzIugs2vwCPZMIHP4aGCrvTiau0NlpF6pKfWqvW3f4mDIiyO5VIj0SEBHDh2Fje3lJMe4fTKmTaGqFgnd3RxEYqZKRH2jqcLNtVxgVpsZ0vB2qakPcZDJml/hgRXxEWb/XKfH0jTLgO1jwOf50EH/4UaovtTie9cWgrPHkebPoXzP42XPusVcCK+KCrM5OoqG/l0z3l1vR1h781vUz6LRUy0iPr8w5T29ze9WpllfugvkT9MSK+aNAQuOpx+OoaGHUJrH4U/jIR3vgKlO6wO530hNMJqx+Dpy+E5hprFOain1lLcov4qPPTYokODeSVDYUQHA7JU61lmKXf0k806ZElO0sJ9HcwZ1R05yeoP0bE98WMgev/CV/fZE052/km/H0mPH8t7P3IepMs3quuFF64Dj74kbWH0FdWwYjz7U4l0msBfg6uzkxiaU4plfUt1vSy4mxoqLQ7mthEhYx0m2maLM0pZfbI6K73PMhbCaHxWv1IpC+IGgZX/AG+tQPO/wkc2gLPXwOPZMDy/7M2VBTv0dEO65+Gx8+xllie9ye46b8wcLDdyURc5vqsFNqdptX0P+ICwIQDn9gdS2yiQka6bU9pPQWHm7ioq9XKju4fM3S2+mNE+pIBUXDud+Gb2+HaZyAyFT7+FfwpHV681Vr1rKPN7pT92/5P4R9z4d3vQNw4+PKnMPVu/SyWPmd0XBgZKZG8vKEAMyEDgiPUJ9OPaR8Z6balOaUAXDg2tvMTDu9Xf4xIXxYQbC0GMOE6qNhrNZBn/xd2vQMhUZB+pfVc6gxweMHGj/3B4QPw4U+s/w8iU+GG/8DYBSpgpE+7ISuFH72xja3F9Uwadi7sW2Z9mKp/9/2ORmSk25bsLGVScgRxXW0Kd/QTkWFzPRdKROwRPRIu+RV8O8eavjTifNj6Ejw3D/48Dhb/0Bqh7Wi3O2nfVJELi75h7f+zb5m1oenX1kP6Qr2Zkz5v/qQEggMcvLyhwJpeVltk/Tch/Y5GZKRbyuqayS6o5sFLRnd9Uu4SGDRM/TEi/Yl/IKTNs75aG2DPYtj+utWrseZxCI6EURfD6MusxvOQSLsT+7b8NdbGlrvfBf9gyLwN5n4PwhPsTibiMeHBAVw+PoFF2cX8dNa5BIP1YWrMad6jSJ+kQka65eOcMoCul11ua4YDy2Hy7R5MJSJeJXAgjL/W+mqps95Y7PnA+tr2Chh+kDLNGrUdNtdaOtU/yO7U3q+1AXa9C+uegsJ1EDIIzv0+TP0ShMbYnU7EFtdnJfPG5iIWFwVxVdQI6+fNOffZHUs8TIWMdMvSnFKSB4UwJi6s8xMOfgbtTdYnriIiQWFWz0z6leDsgKJNsOd92P8JLH8YPv09+IdA6nRrufaUaZA4GYJC7U7uHTrarf0xtr5s9b+0NcKgoXD5w5B5q1U0ivRj5wwbTEpUCC9vKOCqERdA9gvQ3qIPR/oZFTJyRo2t7azIreDmaakYXc293vsR+AVp/xgROZXDD1KmWl8XPgRN1dbywAeWw4FPrRXQAAyHteJW8jRrtCZhEkSPBr9+8ququQYOrIB9H0HO29BQbk3Nm3gDTLwRUs7RhpYiRzgcBtdPSeFPS/ZQnjGTmLanoGAdDNP7kP6kn/x2kN5YkVtBS7uz62WXAfYugaGzIHCA54KJiG8KiYS0K6wvgMbDULTRehNSuM4ahdjwjPWcfzDEpkP8BEiYCDFjrQ07Bwz2/ab21kZrb579n1ijL4UbwOyAgIEw6iKreBl5kT5hFunCtVOS+fPSPbxcMZSvGX7W9DIVMv2KChk5o3e2HiJqYCDTh0d1fkLVQajYA1O+6NlgItI3DIiyFgQYdbH1d2eH9TPl0FYoOfK18y1rueejQgZB9BiIHmUtMBI5xJp6NWio9Zy3FTlNVVC+Bw5lWzuRH8qG8l1gOgEDkibD7G9ZKzAlT7UWURCR00qKDGH2yGj+u6WaryZPw9j3MVz0M7tjiQepkJHTamxtZ+nOUq6ZnESAXxdTGvYusY5H34SIiPSGww9ix1pfk260HjNNqCmEit1WQVCx21pudff70Fhx4usDw6w9VcLiIDQewo58hcZZRVNwpDUqFDIIAkN7V/SYprWwQUM5NFQcOZZby8Ee3m/t83J4PzRXf/6agTGQkAFp8yExw9p3Z0AXHxSJyGndkJXC1/+3mYNjZjJ0yx+h9pBW8etHVMjIaS3NKaOprYMFkxK7Pil3qfVp6OCRngsmIv2LYUBkivV18qIizTVQnW+NDlfnQ/VBqC6wNugt32MdnV3sZ+Pwt6ZyBQRb09gCQqyjXycjIqYT2pqsxvu2piNfDZ1f2/Czsg4aZq3iFjUMokZYfT/hid43YiTioy5OjyMiJIAXaibwY4Dd78HUu+2OJR6iQkZO6+0txcSFBzFtaBefFra3WA27GTfrF7OI2CM4wuqhiZ/Q+fNOJzQdhroSa4pXc7W14MDRP7c2WEVJe/Pnx45W4KSfaYZhjewEhEDAgCNfIdZoysAYGBh95Hjkyy/Avd+3iBAc4Mc1k5N4bk0e348Zhv+ud1XI9CMqZKRLNU1tfLq7nNtnDMHh6KJIObjK+kRypKaViYiXcjiOFBnRdicRETe47Zwh/POzPLaFziLzwEvQXAvB4XbHEg/QOo7SpQ92lNDa4Tz9tLK9S60pGFolRERERGwwIiaUWSMH84/SseBss96bSL+gQka69PaWYlKjBjApOaLrk/YuhSEztTmbiIiI2Ob2c4bwYd0QWoOiYNe7dscRD1EhI52qqG9h1b5KFkxK6HoTzOoCa/lQTSsTERERG100No7Y8AGs9p8KuUugo83uSOIBKmSkU+9vO0SH0zzDtDItuywiIiL28/dzcMv0VP5TNR5aaiBvpd2RxANUyEin3t5yiNFxoaTFn6ZZLncpRKRC9GjPBRMRERHpxE1TU1jDBNocQZpe1k+okJFTFFc3sS7vMAsmnmY0pr0VDnwKoy7SsssiIiJiu9jwYM4dP4QVzok4d79nbVgrfZoKGTnFu1sPAZx+Wln+amitV3+MiIiIeI3bzxnC+22ZOGqL4NAWu+OIm6mQkVO8vbWYickRDI0+zUpkuR+CI0DLLouIiIjXmD4siryoOXTgwNT0sj5PhYyc4EBFA1sLa04/rcw0YeciGH4eBIV5LJuIiIjI6RiGwcKZE9ngHE3z9rftjiNupkJGTvDOlmIA5k9K6Pqk4k1Qkw/jrvJMKBEREZFuuioziU/JIuRwDlTl2R1H3EiFjBxjmiaLthQzbWgUCREhXZ+4401w+MOYKzyWTURERKQ7woID8B83H4CGbRqV6ctUyMgxmwuqyS2r56rMpK5PMk3Y+ZY1rWxAlMeyiYiIiHTXvHNns9uZTNXGN+yOIm6kQkaOeXFdPgMC/ViYcZr+mEPZUH0Q0q/yVCwRERGRHhkTH8buyDnE12ymuabc7jjiJipkBIDa5jbe3nKIhZMSCQ3y7/rEo9PK0uZ5LJuIiIhIT6XOvhl/nGxb8i+7o4ibqJARAN7KLqaprYObp6V2fZJpws43YdhcTSsTERERrzYpaw4H/VIJynkNp1ObY/ZFKmQE0zT539p80hPCmZgc0fWJh7ZYq39oWpmIiIh4OcPhoGHMtUzs2MlnGzbYHUfcQIWMsK2ohp2Harl5eiqGYXR94s43wfCDtPkeyyYiIiJytkZfeCcABZ/+294g4hYqZIT/rcsnJMCPK0/X5G+aVn/MsLkwcLDHsomIiIicLf/BQymJnMK0uqVsOFBpdxxxMRUy/Vx9SzuLsouZPzGB8OCArk/8/+3deXwV1f3/8dcne8KSBAhhC4vIIjuSggtaFcUdcQUrVax1rdXW+rV7XWutWpfWtmrVghsuaCsqiiAomwpB2ZcAIYGwr4GQhJDc8/tjBhv4QXKF3Htzk/fz8bgPZs6cmXwmh7m5nzvnnNm0EHau0UMwRUREJKqknzyK42M28NGUSZEORWqZEpkG7v0FG9hbXsnVg6oZ5A/es2PUrUxERESiTGKfS6mweNoUTGDVluJIhyO1SIlMAzduzlq6ZTahf1bakSsd6FbWcTA0ahGu0ERERESOXXI6lccPZVjsbF6anhvpaKQWKZFpwBavL2JhYRFXD8yqfpD/5sWwY7W6lYmIiEhUSjzxajKsiE3zP2HLnrJIhyO1RIlMA/bG3LUkxsVwaf921Vdc8l+wGOh+cVjiEhEREalVXYZSmZjKxTaDMbPyIx2N1BIlMg1USXkF//1mAxf2bk1qSjWD/J2DJe963coaZ4QvQBEREZHaEpdIbK9LuSAuh/FfrqCoZH+kI5JaoESmgfpgwUaK91XUPMg/fybsyIO+V4cnMBEREZFQ6DOCRFfGyeVf8sLMvEhHI7VAiUwDFAg4XpiZR7fMJmR3SK++8tdjITEVegwPS2wiIiIiIZF1EqS256a0HF6auYbtxfsiHZEco6ASGTM7z8xWmNkqM/vVYbbfZWZLzWyhmX1qZh1qP1SpLdNWbCF3czE3f/+46gf5l+yApROgz5WQkBK+AEVERERqW0wM9LmKHqU5NNq/g+em665MtKsxkTGzWODvwPlAD+BqM+txSLVvgGznXB9gPPBobQcqtefZz1fTNi2Zi/u2qb7iwregch+ceF14AhMREREJpT5XYS7Ab7MWMXZ2Plt2awazaBbMHZmBwCrnXJ5zrhx4A7ikagXn3DTnXIm/+iVQwzRYEinzCnYwN38nNwzuRHxsNc3vnNetrE1/aN0nfAGKiIiIhEpGN2h/Mhfsm0hloJK/T1sV6YjkGASTyLQF1lVZL/TLjuQG4KNjCUpC55+f5ZGWEs/IgVnVVyzMgS1LdTdGRERE6peBNxFflM/vuhby+py1FO4sqXkfqZNqdbC/mY0CsoHHjrD9JjPLMbOcrVu31uaPliCs3LyHKcs2c+3JHUlJiKu+8tdjIL4R9L4iLLGJiIiIhMUJF0OTNox0H2MYz0zVXZloFUwisx6o+vV9O7/sIGZ2NvBbYJhz7rDTQDjnnnfOZTvnsjMy9EyScHtueh5J8TGMPqVj9RXLdsPid6HXZZDYJCyxiYiIiIRFbDxk/4ikgmn8tK/j7XmFrNm2N9JRyVEIJpGZC3Qxs05mlgCMBCZUrWBm/YHn8JKYLbUfphyrjUWlvDd/PSOys2jWKKH6yovHw/4SGDA6LLGJiIiIhNWA0RCbwA0Jk4mPNZ6ekhvpiOQo1JjIOOcqgNuBScAy4C3n3BIze8DMhvnVHgMaA2+b2Xwzm3CEw0mEvDhjDQEHPz7tuJorzxsLLXtC2wGhD0xEREQk3BpnQM/LSFn6FjcOzOC9BRvI3bwn0lHJdxTUGBnn3ETnXFfnXGfn3B/9sj845yb4y2c75zKdc/3817DqjyjhVFSyn3Fz1nJxn9ZkNavheTAbF8DG+TDgOqjuGTMiIiIi0WzQTVBezK1pX9EoIY4/TVwW6YjkO6rVwf5SN736VQF7yyu5+fuda6789csQlwR9rgp9YCIiIiKR0nYAtM0mZf5L3HHWcUxbsZVPl22OdFTyHSiRqedKyyv596w1nNEtgxNaN62+cvleWPg29LgEktPDE6CIiIhIpAy6Gbav4vpW+XTOaMQDHyylbH9lpKOSICmRqedemrWGbcXl/OTM42uu/PXLsK8Ism8IfWAiIiIikdZjODRqSXzOv7hvWE8Ktpfw4sw1kY5KgqREph7bVryPf362mqE9Mvlex2bVV64oh1l/hQ6Dof2g8AQoIiIiEklxCZB9Paz8hNOa7+Hcnpk8M3UVG3aVRjoyCYISmXrs6SkrKd1fyS/P715z5QXjYM8GOO2u0AcmIiIiUlcMuB5iYmHOv/jdhT0IOMcfNfA/KiiRqadWbSnm9TlruWZQezpnNK6+cmUFzHwS2vSHzmeFJ0ARERGRuqBpa+h1OcwbQ1ZCMbee0ZkPF25k9uptkY5MaqBEpp565KPlJMfHcueQLjVXXvpf2LkGTvuFplwWERGRhuf0e6BiH8x8klu+35l26cncP2Ep+ysDkY5MqqFEph76Mm87U5Zt5tYzOtO8cWL1lQMBmPEXyOgO3S4MT4AiIiIidUmL46Hf1TD3RZJKNvG7C3uwYvMeXvmiINKRSTWUyNQzgYDj4YnLaJ2axA2DO9W8Q+7HsGUpDL4LYvTfQURERBqo0+8BF4AZj3Nuz0xO69KCJyfnsnl3WaQjkyPQJ9d65v2FG1hYWMTdQ7uRFB9bfWXnYMbjkNbe6xsqIiIi0lCld4AB18HXL2O7Crh/WE/2BwL88p2FOOciHZ0chhKZeqRsfyWPfryCHq2bcmn/tjXvsGY6rJ8Hp/4MYuNCHp+IiIhInXba3RATB58/ynEZjfn1+Sfw2YqtjJuzLtKRyWEokalHxszOZ/2uUn574QnExAQxaH/G49C4FfS7JvTBiYiIiNR1TVt7DwZfMA62reSHJ3Vg8PEteOjDpRRs3xvp6OQQSmTqiYLte3l6ykqGdG/Jqce3qHmHdXO9OzKn3A7xSaEPUERERCQaDP45xCXBZ38iJsZ49Io+xMYYv3hrAZUBdTGrS5TI1AOBgOP/3l5IXKzx0KW9gtkBJv0GGmV4D4ESEREREU/jDBh0Cyx+FzYvoU1aMg9c0pOcgp08Pz0v0tFJFUpk6oExs/OZk7+D31/Ug9apyTXvsPANKJwDZ98PiTU8LFNERESkoTnlp5DYBKY9DMDwfm25oHcrnpi8gmUbd0c4ODlAiUyUW7NtL49OWs6Z3TK4ckC7mncoK4LJ90K770Hfq0MfoIiIiEi0SWnmJTPLP4DV0zAzHhrem9TkBH7+5nz2VVRGOkJBiUxUCwQc94xfQHxsDH+6rA9mQQzw/+zPsHcrXPCYnhsjIiIiciSn3AHNOsMHP4PyEpo1SuDPl/dm+aY9/OWT3EhHJyiRiWr/np3P3Pyd3HdxT1qlBjFgf8sy+OpZOPFaaNM/9AGKiIiIRKv4JLj4KdiZD5//GYAhJ2Qy6qT2PD89jwkLNkQ0PFEiE7Xythbz2KTlDOnekstODOKZMc7BR/d4/T2H3Bv6AEVERESiXafTof8omP032LQIgD9c1JPvdUznnvELWLy+KMIBNmxKZKJQZcDxf+MXkhAbw8OX9Q6uS9nS97zpls/6HTRqHvogRUREROqDcx70xsxMuAMClSTExfDPUQNolpLATS/nsHXPvkhH2GApkYlCT03JZV7BTu4b1pPMpkF0KSvfC5N+C5m9Nd2yiIiIyHeR0gzOewQ2fA1zngegReNEnr82mx0l5dz66jzKKwIRDrJhUiITZd5fsIG/TV3FVdntuLR/EF3KAKY/DrsL4YJHITYutAGKiIiI1De9LocuQ+HTB2HXWq+obSqPXdGXnIKd3DthMc7pYZnhpkQmiixeX8T/jV9Adod0HhzeK7guZWumw6ynoN810OGUkMcoIiIiUu+YwYV/8ZY/vNsbewxc3LcNt53RmXFz1vHKlwURDLBhUiITJbbsKePGl3NolpLAsz8cQGJcbM07FW+Bd37sTR14/qOhD1JERESkvkpr7401XjkJvn752+K7h3ZjSPeW3P/+Uqat2BLBABseJTJRYF9FJTe/Mo9dJfv513XZtGicWPNOgQC8e5P3AMyrxkJi49AHKiIiIlKfDboZOp8FE++GwhwAYmKMp0b2o3urJtz8yjym526NcJANhxKZOs45x2/eXcw3a3fxxFV96dkmNbgdZ/4F8qZ5d2Iye4Y2SBEREZGGICYWLn8RmrSGN0fBns0ANEmK59UbBtE5ozE3vpzDrFXbIhxow6BEpo7714w83vm6kJ+d3YXze7cObqf8mTDtYeh9pffwSxERERGpHSnNYOTrXq+Xt66FinIA0hsl8NqPB9GxeSNuGDuXL/O2RzjQ+k+JTB02dnY+D09czgW9W3HHWV2C22nvNn9czHFw0ZPe4DQRERERqT2tesElz8C6L+HjX35b3KxRAq/dOIis9BR+NGYuc9bsiGCQ9Z8SmTrqX9PzuHfCEs7pkcmTI/oRExNEQlK5H969EUp2wJVjILFJyOMUERERaZB6XQ6n3AE5L8G8sd8Wt2icyGs3DqJVahLX/3sO8wqUzISKEpk66JmpK/njxGVc2Ls1/7jmxOBmKAtUwn9uhtVT4cLHoVXv0AcqIiIi0pCdfR8cd6Y3+H/tV98Wt2ySxLgbT6Jl0yRGvTCHjxdvilyM9ZgSmTrEOccTn6zg8U9yubR/W54e2Y/42CCaKBCA926Hxe/AOQ9oXIyIiIhIOMTEwhUvQdO28NoVsG7Ot5symybx5s0n0a1VE255dR7PTF2ph2bWMiUydYRzjkc+Xs5fp65iRHYWj1/Zl7hgkhjnYOIvYMHrcMZv4NQ7Qx+siIiIiHhSmsHoD6BRC3jlUsif9e2mlk2SeOOmkxjerw2Pf5LLHW/Mp2x/ZQSDrV+UyNQBpeWV3P32Qp77PI9RJ7XnT5f1JjaYMTHOwce/9vpmDv45fP+e0AcrIiIiIgdLbQfXf+TdmXn1clg97dtNSfGxPDmiH/ec140PFm7gque+YPPusggGW38okYmw1VuLGf73Wbz7TSF3DunCg5f0Cm5gv3Pw6f3w1T9h0K0w5F7NUCYiIiISKU1awegPvZljXx8BuZ98u8nMuO2M43lu1ABWbSlm2DMzycnXJADHSolMBL2/YAPD/jaTrcX7GHv9QH5+TlcsmGRkfxlM+CnMfBIGXA/n/UlJjIiIiEikNc7wupm17A5v/ACWvX/Q5qE9W/HubaeQEBfDlc99wYMfLKW0XF3NjpYSmQjYV1HJ7/+7mJ+O+4burZvy4R2DOb1rRnA77yyAl86Fb16B0+6GC59QEiMiIiJSV6Q0g2snQOu+8OYomHyv94gMX/dWTfnoztO5ZlB7Xpy5hvOfnq7nzRwli9TsCdnZ2S4nJyciPzuSFhUW8Zv/LGLR+iJuPK0T95zXPbiZyQBWTfEedhkIwGXPQbfzQxusiIiIiByd8hKY9GuYNwbafQ8ufwHSOx5UZfbqbfzynYUU7izlupM7cs953UhJiItIuHWVmc1zzmUfdpsSmfDYVryPxyet4M2cdTRvlMBDw3tzXq9Wwe0cCMCMv8C0P0LLHjDiFWjeObQBi4iIiMixW/wuvH8nYDDsaeh56UGb9+6r4NGPlzP2iwKymiXzi3O6cXHfNsFN/NQAKJGJoP2VAV7+ooCnpuRSWl7J6FM6csfZXWiaFB/cATbM92YmWzsb+oyAi56ChJRQhiwiIiIitWlnPoy/AdbnwInXwTn3Q3L6QVW+ytvOfe8vZdnG3XTNbMxd53Tl3J6tghs/XY8pkYmAyoDj02WbeXTSClZtKeb0rhn84aIeHN+ycXAH2LMJPn0Q5r8GKc29J8f2H6XxMCIiIiLRqHI/TH0IZj0NSU29R2cMvPmgL6gDAcfExRt5YnIueVv30qttU34xtBtndM1osAmNEpkw2lO2n7dzChkzO5+1O0ro0DyF31/YgyEntAx+RrIvnoEZT0BlOZx0K5x+NySlhj54EREREQmtTYvh0wdg5SRo0hrO+BX0GwWx/xsbU1EZ4L/zN/DUlFwKd5bSq21TRn6vPZf0a0OTYHv11BNKZMJg7fYSxszO562cdRTvq2BAh3R+dGonzu2ZSVwwg/l3rPFmIvvmVSjeDN0vgnMe0FgYERERkfoofxZMuQ8K50DzLnDybd74mSpdzsorArw9bx2vfFHA8k17SI6P5aI+rRk5sD0ntk9rEHdplMiEyOqtxXyyZDOfLN3EN2t3ERdjXNSnNdef2om+WWk1H6CiHJZ/AF+PhbzPwGKgy1A4+XbodFqowxcRERGRSHIOVkyEaQ/D5sUQm+jNStvvB9B5yLd3aZxzLCgs4o05a5mwYAMl5ZV0admYc3u24szuGfTLSq+3kwMokaklJeUVLCos4rPcrXyyZBOrt+4FoE+7VIb2yOTK7CwymyYd+QDOwdblkD8T8mfAmulQuhNSs6D/D70xMKltw3Q2IiIiIlInOAcbF8CCcbDobSjZDo0yoPuF0P5kyBrkTd1sRvG+Cj5YsIF3vi5kXsFOAg5Sk+M5vWsGZ3TNYHCXFtV/Ho0ySmSOwt59FazZtpcFhbtYsG4XCwuLyN28h4CDuBjjpOOaM7RnJmefkEmbtOSDd67cD0WFULQOdq2FXetg6zLvFmLJNq9O03beXZdeV0DnMyEmNvwnKSIiIiJ1S0U5rJrsJTV502FfkVfeOBOyBkLbAd6X4E3bsDs+g5mb4/l0ZRGf525lW/E+ADKaJNKrTVN6tU2lZ5tUerZpStu0ZGKi8K6NEplqlJZX8tpXBRTuLGX9rlLW7yxlQ1Epu0r+9wTWtJR4+rZLo2+7VPpmpZHdoRmpKUcYaDXuB5D7EbhAlUKDtCzocCp0HOy90jpoBjIRERERObJApdebZ+2XsO4rWPuF9yX5oVKaE7j4byxpMpicgh0sXr+bJRuKWLmlmMqA91k/LsbIbJpEm7QkWqcm0zotiYzGiaQkxJEUH0NSfKz3b1wsmalJdM4IcqbdEDvmRMbMzgOeBmKBF5xzjxyyPRF4GRgAbAdGOOfyqztmXUlkyvZX0v33H9MoIZa26cm0TUumTVoybdOTad8shT5t08hqlhz8YKq5L0DxFi9TTmvvJTBN20FcQmhPRERERETqv7Ii2L0Rdq+HPRth9wbvlf0jaN3n4Kr7K1m+aQ9LNhRRuLOUjbtK2VhUxsaiMjYVlVFeGTjsjxjerw1PjewfjrOp0TElMmYWC+QC5wCFwFzgaufc0ip1bgP6OOduMbORwKXOuRHVHbeuJDIARaX7aZoU1yBmfhARERERCQQce8oqKKuopLS8krKKSsr2ByjbX0l6SgLdWjWJdIhA9YlM3OEKDzEQWOWcy/MP9gZwCbC0Sp1LgPv85fHAM2ZmLlL91r6j1OSGNR+3iIiIiDRsMTFGako8qUTv5+AgHnBCW2BdlfVCv+ywdZxzFUAR0Lw2AhQRERERETlUMHdkao2Z3QTc5K8Wm9mKcP78KNAC2BbpICQoaqvoobaKLmqv6KG2ii5qr+ihtjpYhyNtCCaRWQ9kVVlv55cdrk6hmcUBqXiD/g/inHseeD6In9kgmVnOkfoASt2itooeaqvoovaKHmqr6KL2ih5qq+AF07VsLtDFzDqZWQIwEphwSJ0JwHX+8hXA1GgZHyMiIiIiItGnxjsyzrkKM7sdmIQ3/fJLzrklZvYAkOOcmwC8CLxiZquAHXjJjoiIiIiISEgENUbGOTcRmHhI2R+qLJcBV9ZuaA2Sut1FD7VV9FBbRRe1V/RQW0UXtVf0UFsFKagHYoqIiIiIiNQlwYyRERERERERqVOUyISZmTUzs8lmttL/N/0wdfqZ2RdmtsTMFprZiCrbxpjZGjOb77/6hfUEGgAzO8/MVpjZKjP71WG2J5rZm/72r8ysY5Vtv/bLV5jZuWENvAEKoq3uMrOl/nX0qZl1qLKtssp1dOgEJlLLgmir0Wa2tUqb/LjKtuv898yVZnbdoftK7QuivZ6s0la5ZraryjZdW2FkZi+Z2RYzW3yE7WZmf/XbcqGZnVhlm66tMAqira7x22iRmc02s75VtuX75fPNLCd8Uddxzjm9wvgCHgV+5S//CvjzYep0Bbr4y22AjUCavz4GuCLS51FfX3gTWqwGjgMSgAVAj0Pq3AY86y+PBN70l3v49ROBTv5xYiN9TvX1FWRbnQmk+Mu3Hmgrf7040ufQUF5BttVo4JnD7NsMyPP/TfeX0yN9TvX5FUx7HVL/p3gTAR1Y17UV3vY6HTgRWHyE7RcAHwEGnAR85Zfr2qp7bXXKgTYAzj/QVv56PtAi0udQ1166IxN+lwBj/eWxwPBDKzjncp1zK/3lDcAWICNcATZwA4FVzrk851w58AZem1VVtQ3HA0PMzPzyN5xz+5xza4BV/vEkNGpsK+fcNOdcib/6Jd5zsCT8grmujuRcYLJzbodzbicwGTgvRHGK57u219XAuLBEJv8f59x0vBljj+QS4GXn+RJIM7PW6NoKu5rayjk3228L0N+soCiRCb9M59xGf3kTkFldZTMbiPeN2OoqxX/0bz0+aWaJIYqzoWoLrKuyXuiXHbaOc64CKAKaB7mv1J7v+vu+Ae9byQOSzCzHzL40s+EhiE/+J9i2utx/bxtvZgcexKzrKvyC/p373TU7AVOrFOvaqluO1J66tuq2Q/9mOeATM5tnZjdFKKY6J6jpl+W7MbMpQKvDbPpt1RXnnDOzI04b539j8gpwnXMu4Bf/Gi8BSsCbnu+XwAO1EbdIfWVmo4Bs4PtVijs459ab2XHAVDNb5JxbffgjSBi8D4xzzu0zs5vx7nqeFeGYpGYjgfHOucoqZbq2RI6BmZ2Jl8gMrlI82L+uWgKTzWy5f4enQdMdmRBwzp3tnOt1mNd7wGY/QTmQqGw53DHMrCnwIfBb/1bwgWNv9G8P7wP+jbou1bb1QFaV9XZ+2WHrmFkckApsD3JfqT1B/b7N7Gy8LxGG+dcNAM659f6/ecBnQP9QBtvA1dhWzrntVdrnBWBAsPtKrfsuv/ORHNKtTNdWnXOk9tS1VQeZWR+898BLnHPbD5RXua62AP9Bn/8AJTKRMAE4MDPIdcB7h1YwswS8/6QvO+fGH7LtQBJkeONrDjvzhRy1uUAXM+vkt8NIvDarqmobXgFMdc45v3ykP6tZJ6ALMCdMcTdENbaVmfUHnsNLYrZUKU8/0C3TzFoApwJLwxZ5wxNMW7WusjoMWOYvTwKG+m2WDgz1yyR0gnkfxMy64w0S/6JKma6tumcCcK0/e9lJQJHfxV3XVh1jZu2Bd4EfOudyq5Q3MrMmB5bx2kqf/1DXskh4BHjLzG4ACoCrAMwsG7jFOfdjv+x0oLmZjfb3G+2cmw+8ZmYZeLOPzAduCWv09ZxzrsLMbsd7M4/Fm4lniZk9AOQ45yYALwKvmNkqvEF7I/19l5jZW3h/tCuAnxzS3UJqUZBt9RjQGHjby/1Z65wbBpwAPGdmAbwvdB5xzunDVogE2VZ3mNkwvGtnB94sZjjndpjZg3gfrgEecM5VN7BZjlGQ7QXee98b/hc5B+jaCjMzGwecAbQws0LgXiAewDn3LDARb+ayVUAJcL2/TddWmAXRVn/AG3P7D/9vVoVzLhtvPPV//LI44HXn3MdhP4E6yA5+/xEREREREan71LVMRERERESijhIZERERERGJOkpkREREREQk6iiRERERERGRqKNERkREREREoo4SGRERERERiTpKZERE5KiYWaWZzTezxWb2tpmlfId9R5vZM6GMT0RE6jclMiIicrRKnXP9nHO9gHIOeUCvmemhyyIiEjJKZEREpDbMAI43szPMbIaZTQCWmlmSmf3bzBaZ2TdmdmaVfbLM7DMzW2lm9x7pwGbW0cyWm9lrZrbMzMYfuPtjZkP84y4ys5fMLNEvf8TMlprZQjN7PKRnLiIiEaFERkREjol/5+V8YJFfdCJwp3OuK/ATwDnnegNXA2PNLMmvNxC4HOgDXGlm2dX8mG7AP5xzJwC7gdv844wBRvjHjwNuNbPmwKVAT+dcH+Ch2jtbERGpK5TIiIjI0Uo2s/lADrAWeNEvn+OcW+MvDwZeBXDOLQcKgK7+tsnOue3OuVLgXb/ukaxzzs3yl1/163YD1jjncv3yscDpQBFQBrxoZpcBJcd0liIiUiep/7KIiBytUudcv6oFZgawN8j9XQ3rR1XXOVdhZgOBIcAVwO3AWUHGJCIiUUJ3ZEREJJRmANcAmFlXoD2wwt92jpk1M7NkYDgw67BH8LQ3s5P95R8AM/3jdDSz4/3yHwKfm1ljINU5NxH4OdC3Fs9HRETqCCUyIiISSv8AYsxsEfAmMNo5t8/fNgd4B1gIvOOcy6nmOCuAn5jZMiAd+Kdzrgy4HnjbP34AeBZoAnxgZgvxEp67QnBeIiISYeZcdXfyRUREIsvMOgIf+NM8i4iIALojIyIiIiIiUUh3ZEREpE7wp03+9DCbhjjntoc7HhERqduUyIiIiIiISNRR1zIREREREYk6SmRERERERCTqKJEREREREZGoo0RGRERERESijhIZERERERGJOv8PeqpX1UzOLRgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 8))\n",
    "sns.distplot(labels_with_pred[labels_with_pred.testing_set].Prob_pos, hist=False, label='Test Set')\n",
    "sns.distplot(labels_with_pred[labels_with_pred.training_set].Prob_pos, hist=False, label='Train Set')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Train other Classifiers on the Features for Comparison\n",
    "In order to evaluate our method, let's compare it's power to other algorithms. For now, I'll focus on ones, that operate on the features alone and algorithms that operate on the network alone.\n",
    "The comparison is with:\n",
    "* **SVM classifier**: A very powerful, non-linear method that generally does not really overfit and can deal with little data. This should be an upper bound on what one can reach on the features alone.\n",
    "* **Logistic regression**: This is a linear algorithm, also only operating on the features. It is less powerful than an SVM.\n",
    "* **PageRank**: A network ranking method that only computes the most important nodeso in the network. It's completely blind to the features."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n",
      "findfont: Font family ['Helvetica'] not found. Falling back to DejaVu Sans.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EMOGI EMOGI\n",
      "Random Forest Random_Forest\n",
      "DeepWalk DeepWalk\n",
      "PageRank PageRank\n",
      "Net. Prop. RWR\n",
      "MutSigCV MutSigCV\n",
      "DeepWalk + Features RF RF_dwfeat\n",
      "20/20+ 2020plus\n",
      "GCN Network Only GCN_Featureless\n",
      "Best cutoff for EMOGI Score: 0.73\n"
     ]
    }
   ],
   "source": [
    "best_thr_roc, best_thr_pr = postprocessing.compute_ROC_PR_competitors(model_dir, network_name=network_name,\n",
    "                                                                      plot_correlations=False)\n",
    "print (\"Best cutoff for EMOGI Score: {0:.2f}\".format(best_thr_pr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/project/gcn/diseasegcn/EMOGI/postprocessing.py:676: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  pr_pred_test.drop_duplicates(inplace=True)\n"
     ]
    }
   ],
   "source": [
    "performance_all, performance_test = postprocessing.compute_predictions_competitors(model_dir=model_dir,\n",
    "                                                                                   network_name=network_name,\n",
    "                                                                                   network_measures=False,\n",
    "                                                                                   plot_correlations=False,\n",
    "                                                                                   verbose=False\n",
    "                                                                                  )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7337269395589828\n"
     ]
    }
   ],
   "source": [
    "data = postprocessing.get_training_data(model_dir)\n",
    "network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "cutoff = postprocessing.get_optimal_cutoff(predictions, node_names, test_mask, y_test, colname='Prob_pos')\n",
    "cutoff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EMOGI\n",
      "Random_Forest\n",
      "DeepWalk\n",
      "RF_dwfeat\n",
      "GCN_Featureless\n",
      "2020plus\n",
      "PageRank\n",
      "RWR\n",
      "MutSigCV\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f7e1bdc6850>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score, precision_recall_curve, precision_score, recall_score\n",
    "fig = plt.figure(figsize=(14, 8))\n",
    "y_true = y_test[test_mask == 1, 0]\n",
    "for col in performance_all.drop('ID', axis=1).columns:\n",
    "    print (col)\n",
    "    pr, rec, thresholds = precision_recall_curve(y_true=y_true, probas_pred=performance_test[col])\n",
    "    auc = average_precision_score(y_true=y_true, y_score=performance_test[col])\n",
    "    plt.plot(rec, pr, lw=3, label='{0} (AUC = {1:.2f})'.format(col, auc), alpha=0.8)\n",
    "preds_test = performance_test['EMOGI']\n",
    "prec = precision_score(y_true, preds_test > cutoff)\n",
    "rec = recall_score(y_true, preds_test > cutoff)\n",
    "plt.scatter(rec, prec, s=500, alpha=0.9, c='darkred')\n",
    "plt.xlabel('Recall', fontsize=20)\n",
    "plt.ylabel('Precision', fontsize=20)\n",
    "plt.tick_params(axis='both', labelsize=17)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Compute Overlap with other Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "known_cancer_genes = []\n",
    "candidate_cancer_genes = []\n",
    "n = 0\n",
    "with open('../data/pancancer/NCG/cancergenes_list.txt', 'r') as f:\n",
    "    for line in f.readlines():\n",
    "        n += 1\n",
    "        if n == 1:\n",
    "            continue\n",
    "        l = line.strip().split('\\t')\n",
    "        if len(l) == 2:\n",
    "            known_cancer_genes.append(l[0])\n",
    "            candidate_cancer_genes.append(l[1])\n",
    "        else:\n",
    "            candidate_cancer_genes.append(l[0])\n",
    "\n",
    "known_cancer_genes_innet = nodes[nodes.Name.isin(known_cancer_genes)].Name\n",
    "candidate_cancer_genes_innet = nodes[nodes.Name.isin(candidate_cancer_genes)].Name\n",
    "\n",
    "remove_blood_cancer_genes = False\n",
    "if remove_blood_cancer_genes:\n",
    "    # load cgc\n",
    "    cgc = pd.read_csv('../data/pancancer/cosmic/cancer_gene_census.csv')\n",
    "    cgc.dropna(subset=['Tissue Type'], inplace=True)\n",
    "\n",
    "    # find blood cancer genes based on these abbreviations (E=Epithelial, M=Mesenchymal, O=Other, L=Leukaemia/lymphoma)\n",
    "    pattern = '|'.join(['E', 'O', 'M', 'E;'])\n",
    "    non_blood_cancer_genes = cgc[cgc['Tissue Type'].str.contains(pattern)]\n",
    "    blood_cancer_genes = cgc[~cgc['Tissue Type'].str.contains(pattern)]\n",
    "    known_cancer_genes_innet = non_blood_cancer_genes[non_blood_cancer_genes['Gene Symbol'].isin(known_cancer_genes_innet)]['Gene Symbol']\n",
    "    print (\"Left with {} known cancer genes after blood removal\".format(known_cancer_genes_innet.shape[0]))\n",
    "\n",
    "postprocessing.compute_overlap(model_dir, 'overlap_NCG.svg',\n",
    "                               known_cancer_genes_innet, candidate_cancer_genes_innet,\n",
    "                               .85,\n",
    "                               ['Known Cancer Genes\\n(NCG)', 'Candidate Cancer Genes\\n(NCG)']\n",
    "                              )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Consensus **New** Predictions Across All Networks & Omics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPDB\n",
      "Multinet\n",
      "STRING-db\n",
      "IRefIndex_old\n",
      "IRefIndex\n",
      "PCNet\n",
      "consensus_genes_all_omics.svg\n",
      "consensus_neighbors_all_networks.svg\n",
      "ncgknown_neighbors_consensus.svg\n",
      "consensus_genes_multiomics.svg\n",
      "consensus_genes_achilles_affected_celllines.svg\n",
      "consensus_candidates.tsv\n",
      "consensus_nonaffected_notknown.tsv\n",
      "consensus_nonaffected_notknown_pathways.txt\n",
      "Overall_Survival_FOS.svg\n",
      "Overall_Survival_CDK2.svg\n",
      "Overall_Survival_SP1.svg\n",
      "Overall_Survival_IQGAP1.svg\n",
      "Overall_Survival_YWHAQ.svg\n",
      "Overall_Survival_HDAC2.svg\n",
      "Overall_Survival_FYN.svg\n",
      "Overall_Survival_GRB2.svg\n",
      "Overall_Survival_HDAC1.svg\n",
      "Disease_Free_Survival_YWHAZ.svg\n",
      "survival_LIHC_HDAC1.pdf\n",
      "correlation_cginteractions.svg\n",
      "achilles_effects_genesets.svg\n",
      "consensus_neighbors_all_networks_withrandom.svg\n",
      "consensus_candidates_pathways.txt\n",
      "achilles_effects_twothirds.svg\n",
      "aupr_heatmap.svg\n",
      "212 different non-cancer genes in top 100 predictions across all 7 omics data types!\n"
     ]
    }
   ],
   "source": [
    "all_models_dir = '../data/GCN/training/final_TCGA_all_networks/'\n",
    "top_n = 100\n",
    "all_omics = False\n",
    "\n",
    "\n",
    "consensus_predicted = {}\n",
    "checked_models = 0\n",
    "for network in os.listdir(all_models_dir):\n",
    "    print (network)\n",
    "    network_path = os.path.join(all_models_dir, network)\n",
    "    if all_omics:\n",
    "        if os.path.isdir(network_path) and not network.startswith('.'):\n",
    "            for omics in os.listdir(network_path):\n",
    "                model_path = os.path.join(network_path, omics)\n",
    "                if os.path.isdir(model_path) and not omics.startswith('.'):\n",
    "                    predictions_topn = postprocessing.load_predictions(model_path).head(top_n)\n",
    "                    non_cancer_hits = predictions_topn[~predictions_topn.label]\n",
    "                    for gene in non_cancer_hits.Name:\n",
    "                        if not gene in consensus_predicted:\n",
    "                            consensus_predicted[gene] = 1\n",
    "                        else:\n",
    "                            consensus_predicted[gene] += 1\n",
    "                    checked_models += 1\n",
    "    else:\n",
    "        if os.path.isdir(network_path) and os.path.isdir(os.path.join(network_path, 'multiomics')):\n",
    "            model_path = os.path.join(network_path, 'multiomics')\n",
    "            predictions_topn = postprocessing.load_predictions(model_path).head(top_n)\n",
    "            non_cancer_hits = predictions_topn[~predictions_topn.label]\n",
    "            for gene in non_cancer_hits.Name:\n",
    "                if not gene in consensus_predicted:\n",
    "                    consensus_predicted[gene] = 1\n",
    "                else:\n",
    "                    consensus_predicted[gene] += 1\n",
    "            checked_models += 1\n",
    "\n",
    "print (\"{} different non-cancer genes in top {} predictions across all 7 omics data types!\".format(len(consensus_predicted), top_n))\n",
    "consensus_sorted = sorted(consensus_predicted.items(), key=operator.itemgetter(1), reverse=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sasse/anaconda3/lib/python3.7/site-packages/matplotlib/font_manager.py:1241: UserWarning: findfont: Font family ['Helvetica'] not found. Falling back to DejaVu Sans.\n",
      "  (prop.get_family(), self.defaultFamily[fontext]))\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "to_plot = consensus_sorted[:50]\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h')\n",
    "plt.title('Consensus Top {} Genes Across All Omics & Networks ({} Models)'.format(top_n, checked_models), size=20)\n",
    "plt.xlabel('# of Models where Gene was in Top {} Genes'.format(top_n), size=20)\n",
    "plt.gca().tick_params(axis='y', labelsize=15)\n",
    "fig.savefig(os.path.join(all_models_dir, 'consensus_genes_{}omics.svg'.format('all_' if all_omics else 'multi')))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_cancer_genes_for_net(model_dir):\n",
    "    if model_dir.endswith('.h5'):\n",
    "        data = gcnIO.load_hdf_data(model_dir)\n",
    "    else:\n",
    "        data = _get_training_data(model_dir)\n",
    "    network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "    nodes = pd.DataFrame(node_names, columns=['ID', 'Name'])\n",
    "    nodes['label'] = np.logical_or(np.logical_or(y_train, y_test), y_val)\n",
    "\n",
    "    # get the NCG cancer genes\n",
    "    known_cancer_genes = []\n",
    "    candidate_cancer_genes = []\n",
    "    n = 0\n",
    "    with open('../data/pancancer/NCG/cancergenes_list.txt', 'r') as f:\n",
    "        for line in f.readlines():\n",
    "            n += 1\n",
    "            if n == 1:\n",
    "                continue\n",
    "            l = line.strip().split('\\t')\n",
    "            if len(l) == 2:\n",
    "                known_cancer_genes.append(l[0])\n",
    "                candidate_cancer_genes.append(l[1])\n",
    "            else:\n",
    "                candidate_cancer_genes.append(l[0])\n",
    "    known_cancer_genes_innet = nodes[nodes.Name.isin(known_cancer_genes)].Name\n",
    "    candidate_cancer_genes_innet = nodes[nodes.Name.isin(candidate_cancer_genes)].Name\n",
    "\n",
    "    oncokb_genes = pd.read_csv('/project/gcn/diseasegcn/data/pancancer/oncoKB/cancerGeneList.txt', sep='\\t')\n",
    "    # remove low confidence genes\n",
    "    oncokb_no_ncg_highconf = oncokb_genes#[oncokb_genes['OncoKB Annotated'] == 'Yes']\n",
    "    oncokb_no_ncg_highconf = oncokb_no_ncg_highconf[oncokb_no_ncg_highconf['# of occurrence within resources (Column D-J)'] >= 3]\n",
    "    # remove all NCG genes\n",
    "    oncokb_no_ncg = oncokb_no_ncg_highconf[~oncokb_no_ncg_highconf['Hugo Symbol'].isin(known_cancer_genes_innet)]\n",
    "    candidate_cancer_genes_innet = candidate_cancer_genes_innet[~candidate_cancer_genes_innet.isin(oncokb_no_ncg)]\n",
    "    #oncokb_no_ncg = oncokb_no_ncg[~oncokb_no_ncg['Hugo Symbol'].isin(candidate_cancer_genes_innet)]\n",
    "    # remove genes that are not in the network\n",
    "    oncokb_innet = nodes[nodes.Name.isin(oncokb_no_ncg['Hugo Symbol'])].Name\n",
    "    return oncokb_innet, candidate_cancer_genes_innet, known_cancer_genes_innet\n",
    "\n",
    "def _get_digsee_genes(base_dir, nodes, to_be_removed, threshold):\n",
    "    pan_cancer_genes = []\n",
    "    for f in os.listdir(base_dir):\n",
    "        if f.startswith('mutation') or f.startswith('expression') or f.startswith('methylation'):\n",
    "            ctype = f.split('.')[0].split('_')[1]\n",
    "            fname = os.path.join(base_dir, f)\n",
    "            evidence = pd.read_csv(fname, sep='\\t')\n",
    "            high_scores = evidence[evidence['EVIDENCE SENTENCE SCORE'] >= threshold]\n",
    "            pan_cancer_genes += high_scores['GENE SYMBOL'].tolist()\n",
    "    pan_cancer_genes_filtered = [i for i in pan_cancer_genes if not i in to_be_removed]\n",
    "    return nodes[nodes.Name.isin(pan_cancer_genes)].Name\n",
    "\n",
    "def _get_training_data(training_dir):\n",
    "    args, data_file = gcnIO.load_hyper_params(training_dir)\n",
    "    if os.path.isdir(data_file): # FIXME: This is hacky and not guaranteed to work at all!\n",
    "        network_name = None\n",
    "        for f in os.listdir(data_file):\n",
    "            if network_name is None:\n",
    "                network_name = f.split('_')[0].upper()\n",
    "            else:\n",
    "                assert (f.split('_')[0].upper() == network_name)\n",
    "        fname = '{}_{}.h5'.format(network_name, training_dir.strip('/').split('/')[-1])\n",
    "        data_file = os.path.join(data_file, fname)\n",
    "    data = gcnIO.load_hdf_data(data_file)\n",
    "    return data\n",
    "\n",
    "def get_all_digsee_sets(train_dir, threshold=0.3):\n",
    "    if train_dir.endswith('.h5'):\n",
    "        data = gcnIO.load_hdf_data(train_dir)\n",
    "    else:\n",
    "        data = _get_training_data(train_dir)\n",
    "    network, features, y_train, y_val, y_test, train_mask, val_mask, test_mask, node_names, feat_names = data\n",
    "    nodes = pd.DataFrame(node_names, columns=['ID', 'Name'])\n",
    "    to_be_removed = nodes[np.logical_or(y_train.reshape(-1), y_val.reshape(-1))].Name\n",
    "    expr_genes = _get_digsee_genes('/project/gcn/diseasegcn/data/pancancer/digSEE/expression/', nodes, to_be_removed, threshold)\n",
    "    meth_genes = _get_digsee_genes('/project/gcn/diseasegcn/data/pancancer/digSEE/methylation/', nodes, to_be_removed, threshold)\n",
    "    muta_genes = _get_digsee_genes('/project/gcn/diseasegcn/data/pancancer/digSEE/mutation/', nodes, to_be_removed, threshold)\n",
    "    return muta_genes, meth_genes, expr_genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((945,), (0,), (1709,))"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oncoKB, ncg_cand, ncg_known = get_cancer_genes_for_net(model_dir)\n",
    "ds_mut, ds_meth, ds_expr = get_all_digsee_sets(model_dir, threshold=0.7)\n",
    "ds_mut.shape, ds_meth.shape, ds_expr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'ncg_known' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-14-6ee38bd7a13d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mtop_1000_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpred_ordered\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhead\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mnum_known_in_top\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_1000_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtop_1000_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mName\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncg_known\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mnum_cand_in_top\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_1000_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtop_1000_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mName\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mncg_cand\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mnum_onco_in_top\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtop_1000_pred\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtop_1000_pred\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mName\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moncoKB\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'ncg_known' is not defined"
     ]
    }
   ],
   "source": [
    "top_1000_pred = pred_ordered.head(1000)\n",
    "num_known_in_top = top_1000_pred[top_1000_pred.Name.isin(ncg_known)].shape[0]\n",
    "num_cand_in_top = top_1000_pred[top_1000_pred.Name.isin(ncg_cand)].shape[0]\n",
    "num_onco_in_top = top_1000_pred[top_1000_pred.Name.isin(oncoKB)].shape[0]\n",
    "\n",
    "ds_overlap_top_1000 = [top_1000_pred.Name.isin(omics).sum() for omics in [ds_mut, ds_meth, ds_expr]]\n",
    "all_literature = pd.concat((ncg_known, ncg_cand, oncoKB, ds_mut, ds_meth, ds_expr))\n",
    "\n",
    "print (\"Our top 1000 predictions contain {}/{} known cancer genes\".format(num_known_in_top, known_cancer_genes_innet.shape[0]))\n",
    "print (\"Our top 1000 predictions contain {}/{} candidate cancer genes\".format(num_cand_in_top, candidate_cancer_genes_innet.shape[0]))\n",
    "print (\"Our top 1000 predictions contain {}/{} OncoKB cancer genes\".format(num_onco_in_top, oncoKB.shape[0]))\n",
    "print (\"Our top 1000 predictions contain {}/{} DigSEE mutation pancancer genes\".format(ds_overlap_top_1000[0], ds_mut.shape[0]))\n",
    "print (\"Our top 1000 predictions contain {}/{} DigSEE methylation pancancer genes\".format(ds_overlap_top_1000[1], ds_meth.shape[0]))\n",
    "print (\"Our top 1000 predictions contain {}/{} DigSEE expression pancancer genes\".format(ds_overlap_top_1000[2], ds_expr.shape[0]))\n",
    "print (\"Our top 1000 predictions contain {}/{} literature genes (all of the above)\".format(top_1000_pred.Name.isin(all_literature).sum(), all_literature.shape[0]))\n",
    "print (\"We consider {} genes in total\".format(pred_ordered.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(654,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ncg_known.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Our positive predictions contain 445/654 known cancer genes\n",
      "Our positive predictions contain 383/1287 candidate cancer genes\n",
      "Our positive predictions contain 103/199 OncoKB cancer genes\n",
      "Our positive predictions contain 372/945 DigSEE mutation pancancer genes\n",
      "Our positive predictions contain 0/0 DigSEE methylation pancancer genes\n",
      "Our positive predictions contain 617/1709 DigSEE expression pancancer genes\n",
      "Our positive predictions contain 1272/4794 literature genes (all of the above)\n",
      "We consider 2277 genes in total\n"
     ]
    }
   ],
   "source": [
    "above_thr = pred_ordered[pred_ordered.Mean_Pred >= best_thr_pr]\n",
    "num_known_in_top = above_thr[above_thr.Name.isin(ncg_known)].shape[0]\n",
    "num_cand_in_top = above_thr[above_thr.Name.isin(ncg_cand)].shape[0]\n",
    "num_onco_in_top = above_thr[above_thr.Name.isin(oncoKB)].shape[0]\n",
    "\n",
    "ds_overlap_positive = [above_thr.Name.isin(omics).sum() for omics in [ds_mut, ds_meth, ds_expr]]\n",
    "all_literature = pd.concat((ncg_known, ncg_cand, oncoKB, ds_mut, ds_meth, ds_expr))\n",
    "\n",
    "print (\"Our positive predictions contain {}/{} known cancer genes\".format(num_known_in_top, ncg_known.shape[0]))\n",
    "print (\"Our positive predictions contain {}/{} candidate cancer genes\".format(num_cand_in_top, ncg_cand.shape[0]))\n",
    "print (\"Our positive predictions contain {}/{} OncoKB cancer genes\".format(num_onco_in_top, oncoKB.shape[0]))\n",
    "print (\"Our positive predictions contain {}/{} DigSEE mutation pancancer genes\".format(ds_overlap_positive[0], ds_mut.shape[0]))\n",
    "print (\"Our positive predictions contain {}/{} DigSEE methylation pancancer genes\".format(ds_overlap_positive[1], ds_meth.shape[0]))\n",
    "print (\"Our positive predictions contain {}/{} DigSEE expression pancancer genes\".format(ds_overlap_positive[2], ds_expr.shape[0]))\n",
    "print (\"Our positive predictions contain {}/{} literature genes (all of the above)\".format(above_thr.Name.isin(all_literature).sum(), all_literature.shape[0]))\n",
    "print (\"We consider {} genes in total\".format(above_thr.shape[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_all_neighbors(gene, A, node_names):\n",
    "    nodes = [x[1] for x in node_names]\n",
    "    if gene in nodes:\n",
    "        idx = nodes.index(gene)\n",
    "        interaction_partners_idx = np.where(A[idx, :] == 1)[0]\n",
    "        interaction_partners = node_names[interaction_partners_idx]\n",
    "        return interaction_partners\n",
    "    else:\n",
    "        return [(None, None)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_neighbors = {}\n",
    "for gene, num_predicted in consensus_sorted:\n",
    "    partners = get_all_neighbors(gene, adj, node_names)\n",
    "    for partner_id, partner_name in partners:\n",
    "        if partner_name in consensus_neighbors:\n",
    "            consensus_neighbors[partner_name] += 1\n",
    "        else:\n",
    "            consensus_neighbors[partner_name] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "neighbors_sorted = sorted(consensus_neighbors.items(), key=operator.itemgetter(1), reverse=True)\n",
    "to_plot = neighbors_sorted[:50]\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h')\n",
    "plt.title('Consensus Top 50 Neighbors Across All Omics & Networks'.format(checked_models), size=20)\n",
    "plt.xlabel('# of Consensus Genes (N={}) to which Gene was a Neighbor'.format(len(consensus_sorted)), size=20)\n",
    "plt.gca().tick_params(axis='y', labelsize=15)\n",
    "#fig.savefig(os.path.join(all_models_dir, 'consensus_neighbors_all_networks.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "cancer_gene_neighbors = {}\n",
    "for cancer_gene in known_cancer_genes_innet:\n",
    "    \n",
    "    partners = get_all_neighbors(cancer_gene, adj, node_names)\n",
    "    for partner_id, partner_name in partners:\n",
    "        if partner_name in cancer_gene_neighbors:\n",
    "            cancer_gene_neighbors[partner_name] += 1\n",
    "        else:\n",
    "            cancer_gene_neighbors[partner_name] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cancer_gene_neighbors_sorted = sorted(cancer_gene_neighbors.items(), key=operator.itemgetter(1), reverse=True)\n",
    "to_plot = cancer_gene_neighbors_sorted[:50]\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h')\n",
    "plt.title('Consensus Top 50 Neighbors of Cancer Genes', size=20)\n",
    "plt.xlabel('# of Known Cancer Genes (N={}) to which Gene was a Neighbor'.format(len(known_cancer_genes)), size=20)\n",
    "plt.gca().tick_params(axis='y', labelsize=15)\n",
    "fig.savefig(os.path.join(all_models_dir, 'ncgknown_neighbors_consensus.svg'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Are the Consensus Genes Essential in Cell Lines?\n",
    "In this section, I want to check the following:\n",
    "\n",
    "* In how many cell lines are our consensus genes essential (the population reduces significantly in size; CERES score below -0.5)\n",
    "* How is the correlation between our output probability and the number of affected cell lines\n",
    "* Come up with a list of potentially druggable new cancer genes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>Unnamed: 0</th>\n",
       "      <th>ACH-000004</th>\n",
       "      <th>ACH-000005</th>\n",
       "      <th>ACH-000007</th>\n",
       "      <th>ACH-000009</th>\n",
       "      <th>ACH-000011</th>\n",
       "      <th>ACH-000012</th>\n",
       "      <th>ACH-000013</th>\n",
       "      <th>ACH-000014</th>\n",
       "      <th>ACH-000015</th>\n",
       "      <th>ACH-000017</th>\n",
       "      <th>...</th>\n",
       "      <th>ACH-001736</th>\n",
       "      <th>ACH-001737</th>\n",
       "      <th>ACH-001740</th>\n",
       "      <th>ACH-001745</th>\n",
       "      <th>ACH-001750</th>\n",
       "      <th>ACH-001765</th>\n",
       "      <th>ACH-001814</th>\n",
       "      <th>ACH-001838</th>\n",
       "      <th>ACH-001956</th>\n",
       "      <th>ACH-001957</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Name</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>A1BG</th>\n",
       "      <td>0.169</td>\n",
       "      <td>-0.069</td>\n",
       "      <td>0.054</td>\n",
       "      <td>0.060</td>\n",
       "      <td>0.277</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.062</td>\n",
       "      <td>0.143</td>\n",
       "      <td>-0.091</td>\n",
       "      <td>0.178</td>\n",
       "      <td>...</td>\n",
       "      <td>0.155</td>\n",
       "      <td>-0.050</td>\n",
       "      <td>0.005</td>\n",
       "      <td>0.209</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.136</td>\n",
       "      <td>0.217</td>\n",
       "      <td>-0.086</td>\n",
       "      <td>-0.077</td>\n",
       "      <td>0.056</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A1CF</th>\n",
       "      <td>0.089</td>\n",
       "      <td>0.219</td>\n",
       "      <td>0.081</td>\n",
       "      <td>-0.011</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.167</td>\n",
       "      <td>0.039</td>\n",
       "      <td>-0.036</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.107</td>\n",
       "      <td>...</td>\n",
       "      <td>0.019</td>\n",
       "      <td>0.190</td>\n",
       "      <td>0.349</td>\n",
       "      <td>0.154</td>\n",
       "      <td>0.127</td>\n",
       "      <td>0.021</td>\n",
       "      <td>-0.172</td>\n",
       "      <td>0.083</td>\n",
       "      <td>0.109</td>\n",
       "      <td>0.005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2M</th>\n",
       "      <td>-0.197</td>\n",
       "      <td>0.178</td>\n",
       "      <td>-0.060</td>\n",
       "      <td>-0.054</td>\n",
       "      <td>0.008</td>\n",
       "      <td>0.089</td>\n",
       "      <td>-0.044</td>\n",
       "      <td>0.011</td>\n",
       "      <td>-0.186</td>\n",
       "      <td>-0.068</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.079</td>\n",
       "      <td>-0.195</td>\n",
       "      <td>-0.135</td>\n",
       "      <td>-0.082</td>\n",
       "      <td>-0.107</td>\n",
       "      <td>-0.265</td>\n",
       "      <td>-0.148</td>\n",
       "      <td>0.021</td>\n",
       "      <td>0.068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A2ML1</th>\n",
       "      <td>-0.021</td>\n",
       "      <td>0.158</td>\n",
       "      <td>0.153</td>\n",
       "      <td>0.061</td>\n",
       "      <td>0.446</td>\n",
       "      <td>0.308</td>\n",
       "      <td>0.200</td>\n",
       "      <td>0.183</td>\n",
       "      <td>0.112</td>\n",
       "      <td>0.110</td>\n",
       "      <td>...</td>\n",
       "      <td>0.052</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.218</td>\n",
       "      <td>0.168</td>\n",
       "      <td>0.133</td>\n",
       "      <td>0.076</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.257</td>\n",
       "      <td>0.204</td>\n",
       "      <td>0.199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>A3GALT2</th>\n",
       "      <td>0.039</td>\n",
       "      <td>-0.194</td>\n",
       "      <td>0.087</td>\n",
       "      <td>0.040</td>\n",
       "      <td>-0.037</td>\n",
       "      <td>0.015</td>\n",
       "      <td>-0.070</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.034</td>\n",
       "      <td>-0.196</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.197</td>\n",
       "      <td>0.164</td>\n",
       "      <td>-0.052</td>\n",
       "      <td>-0.130</td>\n",
       "      <td>-0.172</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.124</td>\n",
       "      <td>-0.055</td>\n",
       "      <td>-0.081</td>\n",
       "      <td>-0.043</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 625 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Unnamed: 0 ACH-000004 ACH-000005 ACH-000007 ACH-000009 ACH-000011 ACH-000012  \\\n",
       "Name                                                                           \n",
       "A1BG            0.169     -0.069      0.054      0.060      0.277      0.008   \n",
       "A1CF            0.089      0.219      0.081     -0.011      0.085      0.167   \n",
       "A2M            -0.197      0.178     -0.060     -0.054      0.008      0.089   \n",
       "A2ML1          -0.021      0.158      0.153      0.061      0.446      0.308   \n",
       "A3GALT2         0.039     -0.194      0.087      0.040     -0.037      0.015   \n",
       "\n",
       "Unnamed: 0 ACH-000013 ACH-000014 ACH-000015 ACH-000017  ... ACH-001736  \\\n",
       "Name                                                    ...              \n",
       "A1BG            0.062      0.143     -0.091      0.178  ...      0.155   \n",
       "A1CF            0.039     -0.036      0.008      0.107  ...      0.019   \n",
       "A2M            -0.044      0.011     -0.186     -0.068  ...     -0.125   \n",
       "A2ML1           0.200      0.183      0.112      0.110  ...      0.052   \n",
       "A3GALT2        -0.070     -0.034     -0.034     -0.196  ...     -0.197   \n",
       "\n",
       "Unnamed: 0 ACH-001737 ACH-001740 ACH-001745 ACH-001750 ACH-001765 ACH-001814  \\\n",
       "Name                                                                           \n",
       "A1BG           -0.050      0.005      0.209      0.045      0.136      0.217   \n",
       "A1CF            0.190      0.349      0.154      0.127      0.021     -0.172   \n",
       "A2M            -0.079     -0.195     -0.135     -0.082     -0.107     -0.265   \n",
       "A2ML1           0.101      0.218      0.168      0.133      0.076      0.046   \n",
       "A3GALT2         0.164     -0.052     -0.130     -0.172     -0.117      0.124   \n",
       "\n",
       "Unnamed: 0 ACH-001838 ACH-001956 ACH-001957  \n",
       "Name                                         \n",
       "A1BG           -0.086     -0.077      0.056  \n",
       "A1CF            0.083      0.109      0.005  \n",
       "A2M            -0.148      0.021      0.068  \n",
       "A2ML1           0.257      0.204      0.199  \n",
       "A3GALT2        -0.055     -0.081     -0.043  \n",
       "\n",
       "[5 rows x 625 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "essential_genes = pd.read_csv('../data/pancancer/Achilles/Achilles_gene_effect.csv').T\n",
    "essential_genes.columns = essential_genes.loc['Unnamed: 0']\n",
    "essential_genes.drop('Unnamed: 0', inplace=True)\n",
    "essential_genes['Name'] = [i.split('(')[0].strip() for i in essential_genes.index]\n",
    "essential_genes.set_index('Name', inplace=True)\n",
    "essential_genes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'essential_genes' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-450a8c8c8439>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0mtarget_scores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mpotential_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mconsensus_sorted\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m     \u001b[0mnum_affected_celllines\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_number_of_affected_cellines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpotential_target\u001b[0m\u001b[0;34m,\u001b[0m  \u001b[0messential_genes\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m     \u001b[0mtarget_scores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpotential_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnum_affected_celllines\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'essential_genes' is not defined"
     ]
    }
   ],
   "source": [
    "def get_number_of_affected_cellines(gene_name, achilles_data):\n",
    "    # we only consider negative effects of knockouts because positives are often false\n",
    "    affected_celllines = (achilles_data[achilles_data.index == gene_name] < -0.5).sum().sum()\n",
    "    return affected_celllines\n",
    "\n",
    "target_scores = []\n",
    "for potential_target, _ in consensus_sorted:\n",
    "    num_affected_celllines = get_number_of_affected_cellines(potential_target,  essential_genes)\n",
    "    target_scores.append((potential_target, num_affected_celllines))\n",
    "\n",
    "to_plot = target_scores[:50]\n",
    "fig = plt.figure(figsize=(14, 14))\n",
    "sns.barplot(y=[i[0] for i in to_plot], x=[i[1] for i in to_plot], orient='h')\n",
    "plt.gca().axvline(50, color='black', lw=5, alpha=.8, ls='--')\n",
    "#fig.savefig(os.path.join(all_models_dir, 'consensus_genes_achilles_affected_celllines.svg'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f3408110cf8>"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "gene_effects = (essential_genes < -0.5).sum(axis=1)\n",
    "gene_effects.name = 'Celllines_Affected'\n",
    "pred_with_effects = predictions.join(gene_effects, on='Name')\n",
    "sns.distplot(gene_effects)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, '# Celllines Affected (Ranked)')"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(14, 8))\n",
    "pred_with_effects['Effect_Rank'] = pred_with_effects.Celllines_Affected.rank()\n",
    "pred_with_effects.Effect_Rank.fillna(0, inplace=True)\n",
    "sns.kdeplot(pred_with_effects.Prob_pos.rank(), pred_with_effects.Effect_Rank, cmap='Reds',\n",
    "            shade=True, shade_lowest=False)\n",
    "plt.title('Correlation between # affected celllines & Output Probability (R = {0:.2f})'.format(pred_with_effects.Prob_pos.rank().corr(pred_with_effects.Effect_Rank)), fontsize=25)\n",
    "plt.xlabel('GCN Output Probability (Ranked)', fontsize=20)\n",
    "plt.ylabel('# Celllines Affected (Ranked)', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [],
   "source": [
    "consensus_cancergenes = pd.DataFrame(consensus_sorted, columns=['Name', 'Models_top100']).set_index('Name')\n",
    "\n",
    "# add column for presence in NCG Candidate Cancer Genes\n",
    "consensus_cancergenes['NCG_Candidates'] = consensus_cancergenes.index.isin(candidate_cancer_genes)\n",
    "\n",
    "# add column for presence in OncoKB (anywhere there, not only high confidence)\n",
    "oncokb_genes = pd.read_csv('/project/gcn/diseasegcn/data/pancancer/oncoKB/cancerGeneList.txt', sep='\\t')\n",
    "consensus_cancergenes['OncoKB'] = consensus_cancergenes.index.isin(oncokb_genes['Hugo Symbol'])\n",
    "\n",
    "# add column for the number of cell lines in which the gene is essential (Achilles data)\n",
    "consensus_effects = pd.DataFrame(target_scores, columns=['Name', 'Affected_Celllines']).set_index('Name')\n",
    "consensus_cancergenes = consensus_cancergenes.join(consensus_effects)\n",
    "consensus_cancergenes.to_csv(os.path.join(all_models_dir, 'consensus_candidates.tsv'), sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "high_conf_cancergenes = consensus_cancergenes[~consensus_cancergenes.NCG_Candidates & ~consensus_cancergenes.OncoKB & (consensus_cancergenes.Affected_Celllines < 30)]\n",
    "high_conf_with_ens = high_conf_cancergenes.merge(predictions[['Name', 'label']], how='inner', left_index=True, right_on='Name')\n",
    "high_conf_with_ens.columns = high_conf_with_ens.columns[:-1].tolist() + ['Training_Labels']\n",
    "high_conf_with_ens = high_conf_with_ens[['Name', 'Models_top100', 'Affected_Celllines', 'Training_Labels', 'NCG_Candidates', 'OncoKB']]\n",
    "high_conf_with_ens.to_csv(os.path.join(all_models_dir, 'consensus_nonaffected_notknown.tsv'), sep='\\t')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [],
   "source": [
    "pred_old = postprocessing.load_predictions('../data/GCN/training/final_TCGA_all_networks/CPDB/multiomics/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "723"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_old.head(1000).Name.isin(predictions.head(1000).Name).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
